Impact of Interstock and Rootstock on the Growth and Productivity of Mango (Mangifera indica L.) in the San Lorenzo Valley, Peru

1 Project Setup

library(emmeans)
library(corrplot)
library(multcomp)
library(FSA)
library(factoextra)
library(corrplot)
source('https://inkaverse.com/setup.r')

cat("Project: ", getwd(), "\n")

Project:  C:/Users/LENOVO/git/prochira_injertos 

session_info()

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.4.1 (2024-06-14 ucrt)
 os       Windows 11 x64 (build 22631)
 system   x86_64, mingw32
 ui       RTerm
 language (EN)
 collate  Spanish_Latin America.utf8
 ctype    Spanish_Latin America.utf8
 tz       America/Lima
 date     2024-10-07
 pandoc   3.2 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package       * version  date (UTC) lib source
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 mnormt          2.1.1    2022-09-26 [1] CRAN (R 4.4.0)
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 [1] C:/Users/LENOVO/AppData/Local/R/win-library/4.4
 [2] C:/Program Files/R/R-4.4.1/library

──────────────────────────────────────────────────────────────────────────────

2 Import data

Data were imported from the field book evaluated during the 2017-2019 growing seasons. The evaluations focused on the agronomic traits and fruit biometrics of the mango crop.

url <- "https://docs.google.com/spreadsheets/d/1E_cUmiRFoPLGAj7AQC6HZBD6hXtqzg2UHPvtWr2QR_U/edit#gid=2112492836"

gs <- url %>% 
  as_sheets_id()

ley <- gs %>% 
  range_read("leyenda") %>% 
  rename(tratamientos = TRATAM) %>% 
  rename_with(~ tolower(.))

rdt <- gs %>% 
  range_read("db") %>% 
  merge(., ley) %>% 
  dplyr::select(year = "año", n, tratamientos,n_trat:yema, everything()) %>% 
  rename(treat = tratamientos
         , n_treat = n_trat
         , block = bloque
         , n_plant = n_planta
         , height = alt_planta
         , n_fruits = n_frutos
         , flowering = per_floracion
         , sproud = per_brote
         , scion = yema
         , stock = patron
         , edge = puente
         ) %>% 
  dplyr::arrange(year, n, treat) %>% 
  mutate(across(year:n_plant, ~ as.factor(.))) 

glimpse(rdt)
## Rows: 648
## Columns: 13
## $ year      <fct> 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, …
## $ n         <fct> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1…
## $ treat     <fct> 141, 141, 141, 141, 141, 141, 141, 141, 141, 231, 231, 231, …
## $ n_treat   <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 8, 8, …
## $ stock     <fct> CHULUCANAS, CHULUCANAS, CHULUCANAS, CHULUCANAS, CHULUCANAS, …
## $ edge      <fct> CHULUCANAS, CHULUCANAS, CHULUCANAS, CHULUCANAS, CHULUCANAS, …
## $ scion     <fct> KENT, KENT, KENT, KENT, KENT, KENT, KENT, KENT, KENT, KENT, …
## $ block     <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ n_plant   <fct> 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, …
## $ height    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ n_fruits  <dbl> 170, 200, 310, NA, 235, 185, 180, 132, 80, 231, 198, 195, 20…
## $ flowering <dbl> 60, 50, 50, NA, 80, 60, 40, 90, 90, 60, 70, 40, 90, 80, 80, …
## $ sproud    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
  
fru <-  gs %>% 
  range_read("db_frutos") %>% 
  merge(., ley, ) %>% 
  dplyr::select(year = "año", n, tratamientos,n_trat:yema, everything()) %>% 
  rename(treat = tratamientos
         , n_treat = n_trat
         , block = bloque
         , n_plant = n_planta
         , weigth = peso
         , long = largo
         , n_fruits = n_frutos
         , diameter_1 = diametro_1
         , diameter_2 = diametro_2
         , sample = muestra
         , scion = yema
         , stock = patron
         , edge = puente
         ) %>% 
  dplyr::arrange(year, n, treat) %>% 
  mutate(diameter_average = (diameter_1 + diameter_2)/2) %>% 
  mutate(across(year:sample, ~ as.factor(.)))

glimpse(fru)
## Rows: 240
## Columns: 16
## $ year             <fct> 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023,…
## $ n                <fct> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16…
## $ treat            <fct> 141, 141, 141, 141, 141, 141, 141, 141, 141, 141, 231…
## $ n_treat          <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2,…
## $ stock            <fct> CHULUCANAS, CHULUCANAS, CHULUCANAS, CHULUCANAS, CHULU…
## $ edge             <fct> CHULUCANAS, CHULUCANAS, CHULUCANAS, CHULUCANAS, CHULU…
## $ scion            <fct> KENT, KENT, KENT, KENT, KENT, KENT, KENT, KENT, KENT,…
## $ block            <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ n_plant          <fct> 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 2, 2, 2,…
## $ sample           <fct> 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3,…
## $ n_fruits         <dbl> 26, 26, 26, 26, 26, 35, 35, 35, 35, 35, 118, 118, 118…
## $ weigth           <dbl> 270, 520, 385, 230, 285, 600, 457, 520, 665, 305, 422…
## $ long             <dbl> 86, 111, 103, 86, 88, 116, 101, 115, 126, 91, 100, 10…
## $ diameter_1       <dbl> 78, 90, 86, 70, 78, 96, 88, 88, 103, 78, 91, 84, 84, …
## $ diameter_2       <dbl> 74, 83, 75, 69, 71, 87, 88, 81, 95, 80, 83, 76, 72, 8…
## $ diameter_average <dbl> 76.0, 86.5, 80.5, 69.5, 74.5, 91.5, 88.0, 84.5, 99.0,…

ley %>% kable(caption = "Interstock grafting treatments", align = 'c')

Interstock grafting treatments

n_trat	tratamientos	patron	puente	yema
1	141	CHULUCANAS	CHULUCANAS	KENT
2	231	CHATO	JULIE	KENT
3	241	CHULUCANAS	CHATO	KENT
4	211	CHATO	IRWIN	KENT
5	131	CHULUCANAS	JULIE	KENT
6	111	CHULUCANAS	IRWIN	KENT
7	221	CHATO	CHATO	KENT
8	121	CHATO	CHULUCANAS	KENT

rdt %>% kable(caption = "Evaluation of the agronomic characteristics of mango", align = 'c')

Evaluation of the agronomic characteristics of mango

year	n	treat	n_treat	stock	edge	scion	block	n_plant	height	n_fruits	flowering	sproud
2017	1	141	1	CHULUCANAS	CHULUCANAS	KENT	1	1		170	60
2017	2	141	1	CHULUCANAS	CHULUCANAS	KENT	1	2		200	50
2017	3	141	1	CHULUCANAS	CHULUCANAS	KENT	1	3		310	50
2017	4	141	1	CHULUCANAS	CHULUCANAS	KENT	1	4
2017	5	141	1	CHULUCANAS	CHULUCANAS	KENT	1	5		235	80
2017	6	141	1	CHULUCANAS	CHULUCANAS	KENT	1	6		185	60
2017	7	141	1	CHULUCANAS	CHULUCANAS	KENT	1	7		180	40
2017	8	141	1	CHULUCANAS	CHULUCANAS	KENT	1	8		132	90
2017	9	141	1	CHULUCANAS	CHULUCANAS	KENT	1	9		80	90
2017	10	231	2	CHATO	JULIE	KENT	1	1		231	60
2017	11	231	2	CHATO	JULIE	KENT	1	2		198	70
2017	12	231	2	CHATO	JULIE	KENT	1	3		195	40
2017	13	231	2	CHATO	JULIE	KENT	1	4		200	90
2017	14	231	2	CHATO	JULIE	KENT	1	5		180	80
2017	15	231	2	CHATO	JULIE	KENT	1	6		140	80
2017	16	231	2	CHATO	JULIE	KENT	1	7		200	60
2017	17	231	2	CHATO	JULIE	KENT	1	8		186	75
2017	18	231	2	CHATO	JULIE	KENT	1	9
2017	19	121	8	CHATO	CHULUCANAS	KENT	1	1
2017	20	121	8	CHATO	CHULUCANAS	KENT	1	2		165	75
2017	21	121	8	CHATO	CHULUCANAS	KENT	1	3		210	75
2017	22	121	8	CHATO	CHULUCANAS	KENT	1	4		201	80
2017	23	121	8	CHATO	CHULUCANAS	KENT	1	5		110	80
2017	24	121	8	CHATO	CHULUCANAS	KENT	1	6		215	90
2017	25	121	8	CHATO	CHULUCANAS	KENT	1	7		200	80
2017	26	121	8	CHATO	CHULUCANAS	KENT	1	8		160	80
2017	27	121	8	CHATO	CHULUCANAS	KENT	1	9		162	75
2017	28	211	4	CHATO	IRWIN	KENT	1	1		204	70
2017	29	211	4	CHATO	IRWIN	KENT	1	2		124	80
2017	30	211	4	CHATO	IRWIN	KENT	1	3		130	80
2017	31	211	4	CHATO	IRWIN	KENT	1	4		6	5
2017	32	211	4	CHATO	IRWIN	KENT	1	5		90	35
2017	33	211	4	CHATO	IRWIN	KENT	1	6		170	60
2017	34	211	4	CHATO	IRWIN	KENT	1	7		165	45
2017	35	211	4	CHATO	IRWIN	KENT	1	8		100	25
2017	36	211	4	CHATO	IRWIN	KENT	1	9		70	25
2017	37	131	5	CHULUCANAS	JULIE	KENT	1	1		75	80
2017	38	131	5	CHULUCANAS	JULIE	KENT	1	2		200	80
2017	39	131	5	CHULUCANAS	JULIE	KENT	1	3		184	80
2017	40	131	5	CHULUCANAS	JULIE	KENT	1	4		102	70
2017	41	131	5	CHULUCANAS	JULIE	KENT	1	5		190	60
2017	42	131	5	CHULUCANAS	JULIE	KENT	1	6		180	70
2017	43	131	5	CHULUCANAS	JULIE	KENT	1	7		175	75
2017	44	131	5	CHULUCANAS	JULIE	KENT	1	8		120	80
2017	45	131	5	CHULUCANAS	JULIE	KENT	1	9		155	60
2017	46	241	3	CHULUCANAS	CHATO	KENT	1	1		230	90
2017	47	241	3	CHULUCANAS	CHATO	KENT	1	2		110	90
2017	48	241	3	CHULUCANAS	CHATO	KENT	1	3		185	90
2017	49	241	3	CHULUCANAS	CHATO	KENT	1	4		220	90
2017	50	241	3	CHULUCANAS	CHATO	KENT	1	5		180	90
2017	51	241	3	CHULUCANAS	CHATO	KENT	1	6		175	60
2017	52	241	3	CHULUCANAS	CHATO	KENT	1	7		210	90
2017	53	241	3	CHULUCANAS	CHATO	KENT	1	8		180	90
2017	54	241	3	CHULUCANAS	CHATO	KENT	1	9		178	90
2017	55	111	6	CHULUCANAS	IRWIN	KENT	1	1		150	90
2017	56	111	6	CHULUCANAS	IRWIN	KENT	1	2		160	85
2017	57	111	6	CHULUCANAS	IRWIN	KENT	1	3		200	90
2017	58	111	6	CHULUCANAS	IRWIN	KENT	1	4		140	90
2017	59	111	6	CHULUCANAS	IRWIN	KENT	1	5		175	90
2017	60	111	6	CHULUCANAS	IRWIN	KENT	1	6		200	80
2017	61	111	6	CHULUCANAS	IRWIN	KENT	1	7		208	90
2017	62	111	6	CHULUCANAS	IRWIN	KENT	1	8		300	90
2017	63	111	6	CHULUCANAS	IRWIN	KENT	1	9		230	90
2017	64	221	7	CHATO	CHATO	KENT	1	1		190	90
2017	65	221	7	CHATO	CHATO	KENT	1	2		206	90
2017	66	221	7	CHATO	CHATO	KENT	1	3		145	80
2017	67	221	7	CHATO	CHATO	KENT	1	4		51	20
2017	68	221	7	CHATO	CHATO	KENT	1	5		45	30
2017	69	221	7	CHATO	CHATO	KENT	1	6		163	80
2017	70	221	7	CHATO	CHATO	KENT	1	7		35	15
2017	71	221	7	CHATO	CHATO	KENT	1	8		200	80
2017	72	221	7	CHATO	CHATO	KENT	1	9		32	15
2017	73	211	4	CHATO	IRWIN	KENT	2	1		170	90
2017	74	211	4	CHATO	IRWIN	KENT	2	2		350	80
2017	75	211	4	CHATO	IRWIN	KENT	2	3		200	90
2017	76	211	4	CHATO	IRWIN	KENT	2	4		154	70
2017	77	211	4	CHATO	IRWIN	KENT	2	5		180	90
2017	78	211	4	CHATO	IRWIN	KENT	2	6		130	90
2017	79	211	4	CHATO	IRWIN	KENT	2	7		145	90
2017	80	211	4	CHATO	IRWIN	KENT	2	8		130	90
2017	81	211	4	CHATO	IRWIN	KENT	2	9		220	90
2017	82	121	8	CHATO	CHULUCANAS	KENT	2	1		223	90
2017	83	121	8	CHATO	CHULUCANAS	KENT	2	2		220	90
2017	84	121	8	CHATO	CHULUCANAS	KENT	2	3		230	90
2017	85	121	8	CHATO	CHULUCANAS	KENT	2	4		200	90
2017	86	121	8	CHATO	CHULUCANAS	KENT	2	5		210	90
2017	87	121	8	CHATO	CHULUCANAS	KENT	2	6		150	90
2017	88	121	8	CHATO	CHULUCANAS	KENT	2	7		220	80
2017	89	121	8	CHATO	CHULUCANAS	KENT	2	8		270	90
2017	90	121	8	CHATO	CHULUCANAS	KENT	2	9		170	85
2017	91	231	2	CHATO	JULIE	KENT	2	1		28	90
2017	92	231	2	CHATO	JULIE	KENT	2	2		250	90
2017	93	231	2	CHATO	JULIE	KENT	2	3		115	70
2017	94	231	2	CHATO	JULIE	KENT	2	4		160	80
2017	95	231	2	CHATO	JULIE	KENT	2	5		200	90
2017	96	231	2	CHATO	JULIE	KENT	2	6		150	90
2017	97	231	2	CHATO	JULIE	KENT	2	7		120	90
2017	98	231	2	CHATO	JULIE	KENT	2	8		230	90
2017	99	231	2	CHATO	JULIE	KENT	2	9		240	80
2017	100	141	1	CHULUCANAS	CHULUCANAS	KENT	2	1		210	80
2017	101	141	1	CHULUCANAS	CHULUCANAS	KENT	2	2		205	90
2017	102	141	1	CHULUCANAS	CHULUCANAS	KENT	2	3		210	90
2017	103	141	1	CHULUCANAS	CHULUCANAS	KENT	2	4		170	80
2017	104	141	1	CHULUCANAS	CHULUCANAS	KENT	2	5		120	80
2017	105	141	1	CHULUCANAS	CHULUCANAS	KENT	2	6		240	90
2017	106	141	1	CHULUCANAS	CHULUCANAS	KENT	2	7		280	90
2017	107	141	1	CHULUCANAS	CHULUCANAS	KENT	2	8		260	90
2017	108	141	1	CHULUCANAS	CHULUCANAS	KENT	2	9		170	70
2017	109	221	7	CHATO	CHATO	KENT	2	1		142	90
2017	110	221	7	CHATO	CHATO	KENT	2	2		120	90
2017	111	221	7	CHATO	CHATO	KENT	2	3		140	90
2017	112	221	7	CHATO	CHATO	KENT	2	4		180	90
2017	113	221	7	CHATO	CHATO	KENT	2	5		160	90
2017	114	221	7	CHATO	CHATO	KENT	2	6		130	90
2017	115	221	7	CHATO	CHATO	KENT	2	7		70	90
2017	116	221	7	CHATO	CHATO	KENT	2	8		115	90
2017	117	221	7	CHATO	CHATO	KENT	2	9		104	90
2017	118	111	6	CHULUCANAS	IRWIN	KENT	2	1
2017	119	111	6	CHULUCANAS	IRWIN	KENT	2	2		208	90
2017	120	111	6	CHULUCANAS	IRWIN	KENT	2	3		110	90
2017	121	111	6	CHULUCANAS	IRWIN	KENT	2	4		140	90
2017	122	111	6	CHULUCANAS	IRWIN	KENT	2	5		108	80
2017	123	111	6	CHULUCANAS	IRWIN	KENT	2	6		120	90
2017	124	111	6	CHULUCANAS	IRWIN	KENT	2	7		250	90
2017	125	111	6	CHULUCANAS	IRWIN	KENT	2	8		220	90
2017	126	111	6	CHULUCANAS	IRWIN	KENT	2	9		235	80
2017	127	241	3	CHULUCANAS	CHATO	KENT	2	1		170	80
2017	128	241	3	CHULUCANAS	CHATO	KENT	2	2		182	80
2017	129	241	3	CHULUCANAS	CHATO	KENT	2	3		148	90
2017	130	241	3	CHULUCANAS	CHATO	KENT	2	4		160	90
2017	131	241	3	CHULUCANAS	CHATO	KENT	2	5		23	25
2017	132	241	3	CHULUCANAS	CHATO	KENT	2	6		175	80
2017	133	241	3	CHULUCANAS	CHATO	KENT	2	7		227	90
2017	134	241	3	CHULUCANAS	CHATO	KENT	2	8		180	90
2017	135	241	3	CHULUCANAS	CHATO	KENT	2	9		50	90
2017	136	131	5	CHULUCANAS	JULIE	KENT	2	1		180	80
2017	137	131	5	CHULUCANAS	JULIE	KENT	2	2		240	90
2017	138	131	5	CHULUCANAS	JULIE	KENT	2	3		124	70
2017	139	131	5	CHULUCANAS	JULIE	KENT	2	4		220	85
2017	140	131	5	CHULUCANAS	JULIE	KENT	2	5		240	80
2017	141	131	5	CHULUCANAS	JULIE	KENT	2	6		90	80
2017	142	131	5	CHULUCANAS	JULIE	KENT	2	7		220	85
2017	143	131	5	CHULUCANAS	JULIE	KENT	2	8		250	90
2017	144	131	5	CHULUCANAS	JULIE	KENT	2	9		174	90
2017	145	121	8	CHATO	CHULUCANAS	KENT	3	1		170	85
2017	146	121	8	CHATO	CHULUCANAS	KENT	3	2		198	80
2017	147	121	8	CHATO	CHULUCANAS	KENT	3	3		125	90
2017	148	121	8	CHATO	CHULUCANAS	KENT	3	4		124	90
2017	149	121	8	CHATO	CHULUCANAS	KENT	3	5		120	90
2017	150	121	8	CHATO	CHULUCANAS	KENT	3	6		135	80
2017	151	121	8	CHATO	CHULUCANAS	KENT	3	7		150	90
2017	152	121	8	CHATO	CHULUCANAS	KENT	3	8		330	90
2017	153	121	8	CHATO	CHULUCANAS	KENT	3	9		201	80
2017	154	211	4	CHATO	IRWIN	KENT	3	1		210	90
2017	155	211	4	CHATO	IRWIN	KENT	3	2		100	90
2017	156	211	4	CHATO	IRWIN	KENT	3	3		160	90
2017	157	211	4	CHATO	IRWIN	KENT	3	4		260	90
2017	158	211	4	CHATO	IRWIN	KENT	3	5
2017	159	211	4	CHATO	IRWIN	KENT	3	6		160	85
2017	160	211	4	CHATO	IRWIN	KENT	3	7		210	80
2017	161	211	4	CHATO	IRWIN	KENT	3	8		200	85
2017	162	211	4	CHATO	IRWIN	KENT	3	9		225	90
2017	163	141	1	CHULUCANAS	CHULUCANAS	KENT	3	1		300	90
2017	164	141	1	CHULUCANAS	CHULUCANAS	KENT	3	2		195	70
2017	165	141	1	CHULUCANAS	CHULUCANAS	KENT	3	3		210	80
2017	166	141	1	CHULUCANAS	CHULUCANAS	KENT	3	4		230	80
2017	167	141	1	CHULUCANAS	CHULUCANAS	KENT	3	5		205	90
2017	168	141	1	CHULUCANAS	CHULUCANAS	KENT	3	6		204	80
2017	169	141	1	CHULUCANAS	CHULUCANAS	KENT	3	7		90	90
2017	170	141	1	CHULUCANAS	CHULUCANAS	KENT	3	8		180	70
2017	171	141	1	CHULUCANAS	CHULUCANAS	KENT	3	9		37	20
2017	172	231	2	CHATO	JULIE	KENT	3	1		305	90
2017	173	231	2	CHATO	JULIE	KENT	3	2		120	90
2017	174	231	2	CHATO	JULIE	KENT	3	3		160	80
2017	175	231	2	CHATO	JULIE	KENT	3	4		82	90
2017	176	231	2	CHATO	JULIE	KENT	3	5		110	90
2017	177	231	2	CHATO	JULIE	KENT	3	6		140	80
2017	178	231	2	CHATO	JULIE	KENT	3	7		208	85
2017	179	231	2	CHATO	JULIE	KENT	3	8		240	90
2017	180	231	2	CHATO	JULIE	KENT	3	9		160	90
2017	181	111	6	CHULUCANAS	IRWIN	KENT	3	1		60	40
2017	182	111	6	CHULUCANAS	IRWIN	KENT	3	2		113	50
2017	183	111	6	CHULUCANAS	IRWIN	KENT	3	3		210	80
2017	184	111	6	CHULUCANAS	IRWIN	KENT	3	4		260	90
2017	185	111	6	CHULUCANAS	IRWIN	KENT	3	5		150	80
2017	186	111	6	CHULUCANAS	IRWIN	KENT	3	6		80	50
2017	187	111	6	CHULUCANAS	IRWIN	KENT	3	7		180	90
2017	188	111	6	CHULUCANAS	IRWIN	KENT	3	8		196	80
2017	189	111	6	CHULUCANAS	IRWIN	KENT	3	9		220	80
2017	190	221	7	CHATO	CHATO	KENT	3	1		230	90
2017	191	221	7	CHATO	CHATO	KENT	3	2		210	90
2017	192	221	7	CHATO	CHATO	KENT	3	3		150	85
2017	193	221	7	CHATO	CHATO	KENT	3	4		315	90
2017	194	221	7	CHATO	CHATO	KENT	3	5		220	90
2017	195	221	7	CHATO	CHATO	KENT	3	6
2017	196	221	7	CHATO	CHATO	KENT	3	7		250	90
2017	197	221	7	CHATO	CHATO	KENT	3	8		160	80
2017	198	221	7	CHATO	CHATO	KENT	3	9		230	90
2017	199	131	5	CHULUCANAS	JULIE	KENT	3	1
2017	200	131	5	CHULUCANAS	JULIE	KENT	3	2		280	90
2017	201	131	5	CHULUCANAS	JULIE	KENT	3	3		103	90
2017	202	131	5	CHULUCANAS	JULIE	KENT	3	4		180	90
2017	203	131	5	CHULUCANAS	JULIE	KENT	3	5		250	90
2017	204	131	5	CHULUCANAS	JULIE	KENT	3	6
2017	205	131	5	CHULUCANAS	JULIE	KENT	3	7		190	90
2017	206	131	5	CHULUCANAS	JULIE	KENT	3	8		140	90
2017	207	131	5	CHULUCANAS	JULIE	KENT	3	9		210	80
2017	208	241	3	CHULUCANAS	CHATO	KENT	3	1		190	80
2017	209	241	3	CHULUCANAS	CHATO	KENT	3	2		220	90
2017	210	241	3	CHULUCANAS	CHATO	KENT	3	3		140	80
2017	211	241	3	CHULUCANAS	CHATO	KENT	3	4		210	75
2017	212	241	3	CHULUCANAS	CHATO	KENT	3	5		200	90
2017	213	241	3	CHULUCANAS	CHATO	KENT	3	6		290	90
2017	214	241	3	CHULUCANAS	CHATO	KENT	3	7		140	50
2017	215	241	3	CHULUCANAS	CHATO	KENT	3	8		170	90
2017	216	241	3	CHULUCANAS	CHATO	KENT	3	9		160	85
2018	1	141	1	CHULUCANAS	CHULUCANAS	KENT	1	1			95
2018	2	141	1	CHULUCANAS	CHULUCANAS	KENT	1	2			95
2018	3	141	1	CHULUCANAS	CHULUCANAS	KENT	1	3		315
2018	4	141	1	CHULUCANAS	CHULUCANAS	KENT	1	4
2018	5	141	1	CHULUCANAS	CHULUCANAS	KENT	1	5		230
2018	6	141	1	CHULUCANAS	CHULUCANAS	KENT	1	6		232
2018	7	141	1	CHULUCANAS	CHULUCANAS	KENT	1	7			80
2018	8	141	1	CHULUCANAS	CHULUCANAS	KENT	1	8			90
2018	9	141	1	CHULUCANAS	CHULUCANAS	KENT	1	9			20
2018	10	231	2	CHATO	JULIE	KENT	1	1			80
2018	11	231	2	CHATO	JULIE	KENT	1	2			50
2018	12	231	2	CHATO	JULIE	KENT	1	3		92
2018	13	231	2	CHATO	JULIE	KENT	1	4		163
2018	14	231	2	CHATO	JULIE	KENT	1	5			5
2018	15	231	2	CHATO	JULIE	KENT	1	6		145
2018	16	231	2	CHATO	JULIE	KENT	1	7			80
2018	17	231	2	CHATO	JULIE	KENT	1	8			40
2018	18	231	2	CHATO	JULIE	KENT	1	9
2018	19	121	8	CHATO	CHULUCANAS	KENT	1	1
2018	20	121	8	CHATO	CHULUCANAS	KENT	1	2			90
2018	21	121	8	CHATO	CHULUCANAS	KENT	1	3			90
2018	22	121	8	CHATO	CHULUCANAS	KENT	1	4		295
2018	23	121	8	CHATO	CHULUCANAS	KENT	1	5		382
2018	24	121	8	CHATO	CHULUCANAS	KENT	1	6		270
2018	25	121	8	CHATO	CHULUCANAS	KENT	1	7			90
2018	26	121	8	CHATO	CHULUCANAS	KENT	1	8			90
2018	27	121	8	CHATO	CHULUCANAS	KENT	1	9			50
2018	28	211	4	CHATO	IRWIN	KENT	1	1			50
2018	29	211	4	CHATO	IRWIN	KENT	1	2			60
2018	30	211	4	CHATO	IRWIN	KENT	1	3		242
2018	31	211	4	CHATO	IRWIN	KENT	1	4			70
2018	32	211	4	CHATO	IRWIN	KENT	1	5		320
2018	33	211	4	CHATO	IRWIN	KENT	1	6		300
2018	34	211	4	CHATO	IRWIN	KENT	1	7			80
2018	35	211	4	CHATO	IRWIN	KENT	1	8			90
2018	36	211	4	CHATO	IRWIN	KENT	1	9			95
2018	37	131	5	CHULUCANAS	JULIE	KENT	1	1			25
2018	38	131	5	CHULUCANAS	JULIE	KENT	1	2		250
2018	39	131	5	CHULUCANAS	JULIE	KENT	1	3			80
2018	40	131	5	CHULUCANAS	JULIE	KENT	1	4		240
2018	41	131	5	CHULUCANAS	JULIE	KENT	1	5		200
2018	42	131	5	CHULUCANAS	JULIE	KENT	1	6			10
2018	43	131	5	CHULUCANAS	JULIE	KENT	1	7			75
2018	44	131	5	CHULUCANAS	JULIE	KENT	1	8			60
2018	45	131	5	CHULUCANAS	JULIE	KENT	1	9			90
2018	46	241	3	CHULUCANAS	CHATO	KENT	1	1			20
2018	47	241	3	CHULUCANAS	CHATO	KENT	1	2			40
2018	48	241	3	CHULUCANAS	CHATO	KENT	1	3			70
2018	49	241	3	CHULUCANAS	CHATO	KENT	1	4		250
2018	50	241	3	CHULUCANAS	CHATO	KENT	1	5		195
2018	51	241	3	CHULUCANAS	CHATO	KENT	1	6		280
2018	52	241	3	CHULUCANAS	CHATO	KENT	1	7			30
2018	53	241	3	CHULUCANAS	CHATO	KENT	1	8			80
2018	54	241	3	CHULUCANAS	CHATO	KENT	1	9			90
2018	55	111	6	CHULUCANAS	IRWIN	KENT	1	1			80
2018	56	111	6	CHULUCANAS	IRWIN	KENT	1	2		146
2018	57	111	6	CHULUCANAS	IRWIN	KENT	1	3			15
2018	58	111	6	CHULUCANAS	IRWIN	KENT	1	4		138
2018	59	111	6	CHULUCANAS	IRWIN	KENT	1	5			90
2018	60	111	6	CHULUCANAS	IRWIN	KENT	1	6		190
2018	61	111	6	CHULUCANAS	IRWIN	KENT	1	7
2018	62	111	6	CHULUCANAS	IRWIN	KENT	1	8			80
2018	63	111	6	CHULUCANAS	IRWIN	KENT	1	9			95
2018	64	221	7	CHATO	CHATO	KENT	1	1		180
2018	65	221	7	CHATO	CHATO	KENT	1	2			50
2018	66	221	7	CHATO	CHATO	KENT	1	3			80
2018	67	221	7	CHATO	CHATO	KENT	1	4			80
2018	68	221	7	CHATO	CHATO	KENT	1	5			90
2018	69	221	7	CHATO	CHATO	KENT	1	6		178
2018	70	221	7	CHATO	CHATO	KENT	1	7
2018	71	221	7	CHATO	CHATO	KENT	1	8		204
2018	72	221	7	CHATO	CHATO	KENT	1	9			97
2018	73	211	4	CHATO	IRWIN	KENT	2	1
2018	74	211	4	CHATO	IRWIN	KENT	2	2		266	90
2018	75	211	4	CHATO	IRWIN	KENT	2	3		187	85
2018	76	211	4	CHATO	IRWIN	KENT	2	4			5
2018	77	211	4	CHATO	IRWIN	KENT	2	5		220	90
2018	78	211	4	CHATO	IRWIN	KENT	2	6			35
2018	79	211	4	CHATO	IRWIN	KENT	2	7			90
2018	80	211	4	CHATO	IRWIN	KENT	2	8			95
2018	81	211	4	CHATO	IRWIN	KENT	2	9			90
2018	82	121	8	CHATO	CHULUCANAS	KENT	2	1			85
2018	83	121	8	CHATO	CHULUCANAS	KENT	2	2		108	90
2018	84	121	8	CHATO	CHULUCANAS	KENT	2	3		200	90
2018	85	121	8	CHATO	CHULUCANAS	KENT	2	4			95
2018	86	121	8	CHATO	CHULUCANAS	KENT	2	5			95
2018	87	121	8	CHATO	CHULUCANAS	KENT	2	6			90
2018	88	121	8	CHATO	CHULUCANAS	KENT	2	7		280	85
2018	89	121	8	CHATO	CHULUCANAS	KENT	2	8			90
2018	90	121	8	CHATO	CHULUCANAS	KENT	2	9			90
2018	91	231	2	CHATO	JULIE	KENT	2	1
2018	92	231	2	CHATO	JULIE	KENT	2	2		300	85
2018	93	231	2	CHATO	JULIE	KENT	2	3		290	80
2018	94	231	2	CHATO	JULIE	KENT	2	4			50
2018	95	231	2	CHATO	JULIE	KENT	2	5		214	90
2018	96	231	2	CHATO	JULIE	KENT	2	6			90
2018	97	231	2	CHATO	JULIE	KENT	2	7			30
2018	98	231	2	CHATO	JULIE	KENT	2	8			85
2018	99	231	2	CHATO	JULIE	KENT	2	9			85
2018	100	141	1	CHULUCANAS	CHULUCANAS	KENT	2	1		305	85
2018	101	141	1	CHULUCANAS	CHULUCANAS	KENT	2	2			85
2018	102	141	1	CHULUCANAS	CHULUCANAS	KENT	2	3			75
2018	103	141	1	CHULUCANAS	CHULUCANAS	KENT	2	4			80
2018	104	141	1	CHULUCANAS	CHULUCANAS	KENT	2	5		230	75
2018	105	141	1	CHULUCANAS	CHULUCANAS	KENT	2	6		150	95
2018	106	141	1	CHULUCANAS	CHULUCANAS	KENT	2	7			80
2018	107	141	1	CHULUCANAS	CHULUCANAS	KENT	2	8			95
2018	108	141	1	CHULUCANAS	CHULUCANAS	KENT	2	9			80
2018	109	221	7	CHATO	CHATO	KENT	2	1			60
2018	110	221	7	CHATO	CHATO	KENT	2	2			80
2018	111	221	7	CHATO	CHATO	KENT	2	3			5
2018	112	221	7	CHATO	CHATO	KENT	2	4			3
2018	113	221	7	CHATO	CHATO	KENT	2	5		150	70
2018	114	221	7	CHATO	CHATO	KENT	2	6		140	30
2018	115	221	7	CHATO	CHATO	KENT	2	7			0
2018	116	221	7	CHATO	CHATO	KENT	2	8		150	10
2018	117	221	7	CHATO	CHATO	KENT	2	9			80
2018	118	111	6	CHULUCANAS	IRWIN	KENT	2	1
2018	119	111	6	CHULUCANAS	IRWIN	KENT	2	2			95
2018	120	111	6	CHULUCANAS	IRWIN	KENT	2	3			90
2018	121	111	6	CHULUCANAS	IRWIN	KENT	2	4			85
2018	122	111	6	CHULUCANAS	IRWIN	KENT	2	5			10
2018	123	111	6	CHULUCANAS	IRWIN	KENT	2	6			5
2018	124	111	6	CHULUCANAS	IRWIN	KENT	2	7		160	70
2018	125	111	6	CHULUCANAS	IRWIN	KENT	2	8		325	90
2018	126	111	6	CHULUCANAS	IRWIN	KENT	2	9		265	95
2018	127	241	3	CHULUCANAS	CHATO	KENT	2	1			80
2018	128	241	3	CHULUCANAS	CHATO	KENT	2	2			20
2018	129	241	3	CHULUCANAS	CHATO	KENT	2	3		65	20
2018	130	241	3	CHULUCANAS	CHATO	KENT	2	4		61	20
2018	131	241	3	CHULUCANAS	CHATO	KENT	2	5
2018	132	241	3	CHULUCANAS	CHATO	KENT	2	6		255	95
2018	133	241	3	CHULUCANAS	CHATO	KENT	2	7			40
2018	134	241	3	CHULUCANAS	CHATO	KENT	2	8			60
2018	135	241	3	CHULUCANAS	CHATO	KENT	2	9			50
2018	136	131	5	CHULUCANAS	JULIE	KENT	2	1			20
2018	137	131	5	CHULUCANAS	JULIE	KENT	2	2			95
2018	138	131	5	CHULUCANAS	JULIE	KENT	2	3		124	90
2018	139	131	5	CHULUCANAS	JULIE	KENT	2	4		220	90
2018	140	131	5	CHULUCANAS	JULIE	KENT	2	5		260	95
2018	141	131	5	CHULUCANAS	JULIE	KENT	2	6
2018	142	131	5	CHULUCANAS	JULIE	KENT	2	7			90
2018	143	131	5	CHULUCANAS	JULIE	KENT	2	8			95
2018	144	131	5	CHULUCANAS	JULIE	KENT	2	9			95
2018	145	121	8	CHATO	CHULUCANAS	KENT	3	1			95
2018	146	121	8	CHATO	CHULUCANAS	KENT	3	2			95
2018	147	121	8	CHATO	CHULUCANAS	KENT	3	3			95
2018	148	121	8	CHATO	CHULUCANAS	KENT	3	4		110	90
2018	149	121	8	CHATO	CHULUCANAS	KENT	3	5		147	95
2018	150	121	8	CHATO	CHULUCANAS	KENT	3	6			100
2018	151	121	8	CHATO	CHULUCANAS	KENT	3	7			75
2018	152	121	8	CHATO	CHULUCANAS	KENT	3	8		390	90
2018	153	121	8	CHATO	CHULUCANAS	KENT	3	9			95
2018	154	211	4	CHATO	IRWIN	KENT	3	1			80
2018	155	211	4	CHATO	IRWIN	KENT	3	2			95
2018	156	211	4	CHATO	IRWIN	KENT	3	3			40
2018	157	211	4	CHATO	IRWIN	KENT	3	4		160	65
2018	158	211	4	CHATO	IRWIN	KENT	3	5
2018	159	211	4	CHATO	IRWIN	KENT	3	6		126	75
2018	160	211	4	CHATO	IRWIN	KENT	3	7			20
2018	161	211	4	CHATO	IRWIN	KENT	3	8		136	25
2018	162	211	4	CHATO	IRWIN	KENT	3	9			40
2018	163	141	1	CHULUCANAS	CHULUCANAS	KENT	3	1		390	80
2018	164	141	1	CHULUCANAS	CHULUCANAS	KENT	3	2			95
2018	165	141	1	CHULUCANAS	CHULUCANAS	KENT	3	3			90
2018	166	141	1	CHULUCANAS	CHULUCANAS	KENT	3	4			70
2018	167	141	1	CHULUCANAS	CHULUCANAS	KENT	3	5		250	95
2018	168	141	1	CHULUCANAS	CHULUCANAS	KENT	3	6			95
2018	169	141	1	CHULUCANAS	CHULUCANAS	KENT	3	7			0
2018	170	141	1	CHULUCANAS	CHULUCANAS	KENT	3	8		272	90
2018	171	141	1	CHULUCANAS	CHULUCANAS	KENT	3	9			95
2018	172	231	2	CHATO	JULIE	KENT	3	1		203	50
2018	173	231	2	CHATO	JULIE	KENT	3	2			0
2018	174	231	2	CHATO	JULIE	KENT	3	3			0
2018	175	231	2	CHATO	JULIE	KENT	3	4			5
2018	176	231	2	CHATO	JULIE	KENT	3	5			2
2018	177	231	2	CHATO	JULIE	KENT	3	6		33	15
2018	178	231	2	CHATO	JULIE	KENT	3	7		116	40
2018	179	231	2	CHATO	JULIE	KENT	3	8			60
2018	180	231	2	CHATO	JULIE	KENT	3	9			50
2018	181	111	6	CHULUCANAS	IRWIN	KENT	3	1			90
2018	182	111	6	CHULUCANAS	IRWIN	KENT	3	2			95
2018	183	111	6	CHULUCANAS	IRWIN	KENT	3	3		245	90
2018	184	111	6	CHULUCANAS	IRWIN	KENT	3	4		290	90
2018	185	111	6	CHULUCANAS	IRWIN	KENT	3	5			70
2018	186	111	6	CHULUCANAS	IRWIN	KENT	3	6			85
2018	187	111	6	CHULUCANAS	IRWIN	KENT	3	7			90
2018	188	111	6	CHULUCANAS	IRWIN	KENT	3	8			80
2018	189	111	6	CHULUCANAS	IRWIN	KENT	3	9		160	50
2018	190	221	7	CHATO	CHATO	KENT	3	1		220	80
2018	191	221	7	CHATO	CHATO	KENT	3	2			10
2018	192	221	7	CHATO	CHATO	KENT	3	3			0
2018	193	221	7	CHATO	CHATO	KENT	3	4		182	50
2018	194	221	7	CHATO	CHATO	KENT	3	5			0
2018	195	221	7	CHATO	CHATO	KENT	3	6
2018	196	221	7	CHATO	CHATO	KENT	3	7		180	80
2018	197	221	7	CHATO	CHATO	KENT	3	8			5
2018	198	221	7	CHATO	CHATO	KENT	3	9			2
2018	199	131	5	CHULUCANAS	JULIE	KENT	3	1
2018	200	131	5	CHULUCANAS	JULIE	KENT	3	2			85
2018	201	131	5	CHULUCANAS	JULIE	KENT	3	3			70
2018	202	131	5	CHULUCANAS	JULIE	KENT	3	4		213	70
2018	203	131	5	CHULUCANAS	JULIE	KENT	3	5		300	90
2018	204	131	5	CHULUCANAS	JULIE	KENT	3	6
2018	205	131	5	CHULUCANAS	JULIE	KENT	3	7			10
2018	206	131	5	CHULUCANAS	JULIE	KENT	3	8			20
2018	207	131	5	CHULUCANAS	JULIE	KENT	3	9		200	60
2018	208	241	3	CHULUCANAS	CHATO	KENT	3	1			80
2018	209	241	3	CHULUCANAS	CHATO	KENT	3	2			25
2018	210	241	3	CHULUCANAS	CHATO	KENT	3	3			60
2018	211	241	3	CHULUCANAS	CHATO	KENT	3	4		210	60
2018	212	241	3	CHULUCANAS	CHATO	KENT	3	5		75	15
2018	213	241	3	CHULUCANAS	CHATO	KENT	3	6		188	40
2018	214	241	3	CHULUCANAS	CHATO	KENT	3	7			10
2018	215	241	3	CHULUCANAS	CHATO	KENT	3	8			80
2018	216	241	3	CHULUCANAS	CHATO	KENT	3	9			40
2019	1	141	1	CHULUCANAS	CHULUCANAS	KENT	1	1	3.15	180	95	80
2019	2	141	1	CHULUCANAS	CHULUCANAS	KENT	1	2	3.60	280	90	75
2019	3	141	1	CHULUCANAS	CHULUCANAS	KENT	1	3	3.65	234	80	70
2019	4	141	1	CHULUCANAS	CHULUCANAS	KENT	1	4
2019	5	141	1	CHULUCANAS	CHULUCANAS	KENT	1	5	3.00	110	90	40
2019	6	141	1	CHULUCANAS	CHULUCANAS	KENT	1	6	3.40	202	95	80
2019	7	141	1	CHULUCANAS	CHULUCANAS	KENT	1	7	3.40	202	80	75
2019	8	141	1	CHULUCANAS	CHULUCANAS	KENT	1	8	3.20	108	70	50
2019	9	141	1	CHULUCANAS	CHULUCANAS	KENT	1	9	2.70	80	90	0
2019	10	231	2	CHATO	JULIE	KENT	1	1	3.65	245	85	5
2019	11	231	2	CHATO	JULIE	KENT	1	2	3.95	228	80	25
2019	12	231	2	CHATO	JULIE	KENT	1	3	4.85	320	95	20
2019	13	231	2	CHATO	JULIE	KENT	1	4	4.35	307	90	40
2019	14	231	2	CHATO	JULIE	KENT	1	5	4.00	251	80	75
2019	15	231	2	CHATO	JULIE	KENT	1	6	4.20	264	70	35
2019	16	231	2	CHATO	JULIE	KENT	1	7	4.25	280	95	65
2019	17	231	2	CHATO	JULIE	KENT	1	8	4.80	288	95	80
2019	18	231	2	CHATO	JULIE	KENT	1	9				70
2019	19	121	8	CHATO	CHULUCANAS	KENT	1	1	4.05			70
2019	20	121	8	CHATO	CHULUCANAS	KENT	1	2	4.00	122	90	80
2019	21	121	8	CHATO	CHULUCANAS	KENT	1	3	3.70	118	90	80
2019	22	121	8	CHATO	CHULUCANAS	KENT	1	4	4.25	152	90	75
2019	23	121	8	CHATO	CHULUCANAS	KENT	1	5	4.35	333	90	40
2019	24	121	8	CHATO	CHULUCANAS	KENT	1	6	3.70	221	95	75
2019	25	121	8	CHATO	CHULUCANAS	KENT	1	7	4.50	304	85	90
2019	26	121	8	CHATO	CHULUCANAS	KENT	1	8	4.15	182	80	90
2019	27	121	8	CHATO	CHULUCANAS	KENT	1	9	3.95	210	95	70
2019	28	211	4	CHATO	IRWIN	KENT	1	1	4.20	395	90	30
2019	29	211	4	CHATO	IRWIN	KENT	1	2	3.95	160	90	75
2019	30	211	4	CHATO	IRWIN	KENT	1	3	4.15	226	90	70
2019	31	211	4	CHATO	IRWIN	KENT	1	4	4.45	194	85	70
2019	32	211	4	CHATO	IRWIN	KENT	1	5	4.10	315	75	60
2019	33	211	4	CHATO	IRWIN	KENT	1	6	4.45	130	65	30
2019	34	211	4	CHATO	IRWIN	KENT	1	7	4.40	175	80	30
2019	35	211	4	CHATO	IRWIN	KENT	1	8	4.00	140	80	60
2019	36	211	4	CHATO	IRWIN	KENT	1	9	3.80	97	65	60
2019	37	131	5	CHULUCANAS	JULIE	KENT	1	1	3.00	125	90	80
2019	38	131	5	CHULUCANAS	JULIE	KENT	1	2	3.95	185	70	65
2019	39	131	5	CHULUCANAS	JULIE	KENT	1	3	4.10	203	80	90
2019	40	131	5	CHULUCANAS	JULIE	KENT	1	4	3.65	160	90	45
2019	41	131	5	CHULUCANAS	JULIE	KENT	1	5	3.65	137	90	60
2019	42	131	5	CHULUCANAS	JULIE	KENT	1	6	3.10	138	95	65
2019	43	131	5	CHULUCANAS	JULIE	KENT	1	7	3.70	184	95	80
2019	44	131	5	CHULUCANAS	JULIE	KENT	1	8	3.60	144	90	80
2019	45	131	5	CHULUCANAS	JULIE	KENT	1	9	3.80	210	85	75
2019	46	241	3	CHULUCANAS	CHATO	KENT	1	1	4.45	219	90	80
2019	47	241	3	CHULUCANAS	CHATO	KENT	1	2	4.65	240	90	70
2019	48	241	3	CHULUCANAS	CHATO	KENT	1	3	4.25	265	95	85
2019	49	241	3	CHULUCANAS	CHATO	KENT	1	4	4.35	215	95	75
2019	50	241	3	CHULUCANAS	CHATO	KENT	1	5	4.45	361	90	75
2019	51	241	3	CHULUCANAS	CHATO	KENT	1	6	4.05	144	80	75
2019	52	241	3	CHULUCANAS	CHATO	KENT	1	7	4.35	215	85	70
2019	53	241	3	CHULUCANAS	CHATO	KENT	1	8	4.10	104	90	45
2019	54	241	3	CHULUCANAS	CHATO	KENT	1	9	4.05	97	70	75
2019	55	111	6	CHULUCANAS	IRWIN	KENT	1	1	4.30	76	60	40
2019	56	111	6	CHULUCANAS	IRWIN	KENT	1	2	3.65	220	90	65
2019	57	111	6	CHULUCANAS	IRWIN	KENT	1	3	4.10	245	80	60
2019	58	111	6	CHULUCANAS	IRWIN	KENT	1	4	3.70	220	95	70
2019	59	111	6	CHULUCANAS	IRWIN	KENT	1	5	3.40	98	80	30
2019	60	111	6	CHULUCANAS	IRWIN	KENT	1	6	3.85	172	95	80
2019	61	111	6	CHULUCANAS	IRWIN	KENT	1	7
2019	62	111	6	CHULUCANAS	IRWIN	KENT	1	8	3.45	148	80	80
2019	63	111	6	CHULUCANAS	IRWIN	KENT	1	9
2019	64	221	7	CHATO	CHATO	KENT	1	1	4.45	206	95	20
2019	65	221	7	CHATO	CHATO	KENT	1	2	3.65	260	95	50
2019	66	221	7	CHATO	CHATO	KENT	1	3	3.40	132	95	70
2019	67	221	7	CHATO	CHATO	KENT	1	4	3.15	25	70
2019	68	221	7	CHATO	CHATO	KENT	1	5	3.35	51	80	70
2019	69	221	7	CHATO	CHATO	KENT	1	6	4.25	210	95	70
2019	70	221	7	CHATO	CHATO	KENT	1	7	3.30	65	80	80
2019	71	221	7	CHATO	CHATO	KENT	1	8	3.85	217	80	70
2019	72	221	7	CHATO	CHATO	KENT	1	9	3.00	65	85	70
2019	73	211	4	CHATO	IRWIN	KENT	2	1	4.85	23	30	20
2019	74	211	4	CHATO	IRWIN	KENT	2	2	4.05	164	90	70
2019	75	211	4	CHATO	IRWIN	KENT	2	3	3.95	187	70	70
2019	76	211	4	CHATO	IRWIN	KENT	2	4	3.10	34	70	40
2019	77	211	4	CHATO	IRWIN	KENT	2	5	3.05	167	80	65
2019	78	211	4	CHATO	IRWIN	KENT	2	6	3.40	130	90	45
2019	79	211	4	CHATO	IRWIN	KENT	2	7	4.05	312	60	60
2019	80	211	4	CHATO	IRWIN	KENT	2	8	3.75	136	60	40
2019	81	211	4	CHATO	IRWIN	KENT	2	9	3.70	139	85	65
2019	82	121	8	CHATO	CHULUCANAS	KENT	2	1	3.45	185	85	70
2019	83	121	8	CHATO	CHULUCANAS	KENT	2	2	3.75	58	90	50
2019	84	121	8	CHATO	CHULUCANAS	KENT	2	3	3.70	118	90	65
2019	85	121	8	CHATO	CHULUCANAS	KENT	2	4	3.75	152	70	60
2019	86	121	8	CHATO	CHULUCANAS	KENT	2	5	3.65	75	90
2019	87	121	8	CHATO	CHULUCANAS	KENT	2	6	4.05	187	90	70
2019	88	121	8	CHATO	CHULUCANAS	KENT	2	7	3.95	120	80	80
2019	89	121	8	CHATO	CHULUCANAS	KENT	2	8	3.40	150	90	40
2019	90	121	8	CHATO	CHULUCANAS	KENT	2	9	2.90	87	70	70
2019	91	231	2	CHATO	JULIE	KENT	2	1	2.00
2019	92	231	2	CHATO	JULIE	KENT	2	2	3.90	142	95	70
2019	93	231	2	CHATO	JULIE	KENT	2	3	3.90	260	90	75
2019	94	231	2	CHATO	JULIE	KENT	2	4	3.10	124	90	20
2019	95	231	2	CHATO	JULIE	KENT	2	5	3.60	132	80	70
2019	96	231	2	CHATO	JULIE	KENT	2	6	3.85	103	60	65
2019	97	231	2	CHATO	JULIE	KENT	2	7	3.65	104	85	65
2019	98	231	2	CHATO	JULIE	KENT	2	8	3.70	123	60	80
2019	99	231	2	CHATO	JULIE	KENT	2	9	3.35	139	70	90
2019	100	141	1	CHULUCANAS	CHULUCANAS	KENT	2	1	3.85	260	90	75
2019	101	141	1	CHULUCANAS	CHULUCANAS	KENT	2	2	3.55	215	90	35
2019	102	141	1	CHULUCANAS	CHULUCANAS	KENT	2	3	3.80	103	90	75
2019	103	141	1	CHULUCANAS	CHULUCANAS	KENT	2	4	3.45	181	90	65
2019	104	141	1	CHULUCANAS	CHULUCANAS	KENT	2	5	4.15	360	80	80
2019	105	141	1	CHULUCANAS	CHULUCANAS	KENT	2	6	3.45	98	60	20
2019	106	141	1	CHULUCANAS	CHULUCANAS	KENT	2	7	3.40	229	95	70
2019	107	141	1	CHULUCANAS	CHULUCANAS	KENT	2	8	3.30	159	90	70
2019	108	141	1	CHULUCANAS	CHULUCANAS	KENT	2	9	3.40	211	90	60
2019	109	221	7	CHATO	CHATO	KENT	2	1	3.35	88	70	40
2019	110	221	7	CHATO	CHATO	KENT	2	2	3.15	164	80	50
2019	111	221	7	CHATO	CHATO	KENT	2	3	3.30	127	70	50
2019	112	221	7	CHATO	CHATO	KENT	2	4	4.25	130	90	30
2019	113	221	7	CHATO	CHATO	KENT	2	5	2.80	218	70	65
2019	114	221	7	CHATO	CHATO	KENT	2	6	4.35	280	90	50
2019	115	221	7	CHATO	CHATO	KENT	2	7	3.95	125	80	50
2019	116	221	7	CHATO	CHATO	KENT	2	8	4.35	150	80	40
2019	117	221	7	CHATO	CHATO	KENT	2	9	3.50	102	80	50
2019	118	111	6	CHULUCANAS	IRWIN	KENT	2	1
2019	119	111	6	CHULUCANAS	IRWIN	KENT	2	2	3.30
2019	120	111	6	CHULUCANAS	IRWIN	KENT	2	3	3.20	95	90	60
2019	121	111	6	CHULUCANAS	IRWIN	KENT	2	4	3.60	134	70	60
2019	122	111	6	CHULUCANAS	IRWIN	KENT	2	5	3.70	205	70	30
2019	123	111	6	CHULUCANAS	IRWIN	KENT	2	6	4.10	152	90	30
2019	124	111	6	CHULUCANAS	IRWIN	KENT	2	7	3.90	125	90	60
2019	125	111	6	CHULUCANAS	IRWIN	KENT	2	8	4.20	219	70	80
2019	126	111	6	CHULUCANAS	IRWIN	KENT	2	9	3.50	241	80	80
2019	127	241	3	CHULUCANAS	CHATO	KENT	2	1	3.30	172	60
2019	128	241	3	CHULUCANAS	CHATO	KENT	2	2	3.50	220	85	75
2019	129	241	3	CHULUCANAS	CHATO	KENT	2	3	3.85	152	90	70
2019	130	241	3	CHULUCANAS	CHATO	KENT	2	4	3.95	130	90	50
2019	131	241	3	CHULUCANAS	CHATO	KENT	2	5	1.90	3	60	90
2019	132	241	3	CHULUCANAS	CHATO	KENT	2	6	1.85	216	80	70
2019	133	241	3	CHULUCANAS	CHATO	KENT	2	7	4.30	181	90	90
2019	134	241	3	CHULUCANAS	CHATO	KENT	2	8	3.45	176	80	70
2019	135	241	3	CHULUCANAS	CHATO	KENT	2	9	2.75	75	90	10
2019	136	131	5	CHULUCANAS	JULIE	KENT	2	1	2.75	120	90	70
2019	137	131	5	CHULUCANAS	JULIE	KENT	2	2	3.50	151	50	70
2019	138	131	5	CHULUCANAS	JULIE	KENT	2	3	3.40	135	80	80
2019	139	131	5	CHULUCANAS	JULIE	KENT	2	4	3.65	250	70	80
2019	140	131	5	CHULUCANAS	JULIE	KENT	2	5	3.10	188	70	75
2019	141	131	5	CHULUCANAS	JULIE	KENT	2	6
2019	142	131	5	CHULUCANAS	JULIE	KENT	2	7	2.90	121	90	40
2019	143	131	5	CHULUCANAS	JULIE	KENT	2	8	3.45	138	70	80
2019	144	131	5	CHULUCANAS	JULIE	KENT	2	9	2.90	145	90	70
2019	145	121	8	CHATO	CHULUCANAS	KENT	3	1	3.20	271	50	50
2019	146	121	8	CHATO	CHULUCANAS	KENT	3	2	3.40	221	40	60
2019	147	121	8	CHATO	CHULUCANAS	KENT	3	3	3.60	208	80	70
2019	148	121	8	CHATO	CHULUCANAS	KENT	3	4	3.30	168	50	45
2019	149	121	8	CHATO	CHULUCANAS	KENT	3	5	3.00	42	7	15
2019	150	121	8	CHATO	CHULUCANAS	KENT	3	6	2.85	150	50	65
2019	151	121	8	CHATO	CHULUCANAS	KENT	3	7	3.65	214	70	75
2019	152	121	8	CHATO	CHULUCANAS	KENT	3	8	3.65	346	70	80
2019	153	121	8	CHATO	CHULUCANAS	KENT	3	9	3.00	190	50	60
2019	154	211	4	CHATO	IRWIN	KENT	3	1	4.20	400	90	80
2019	155	211	4	CHATO	IRWIN	KENT	3	2	3.30	183	60
2019	156	211	4	CHATO	IRWIN	KENT	3	3	4.30	263	90	10
2019	157	211	4	CHATO	IRWIN	KENT	3	4	3.30	250	55	70
2019	158	211	4	CHATO	IRWIN	KENT	3	5
2019	159	211	4	CHATO	IRWIN	KENT	3	6	3.65	262	90	65
2019	160	211	4	CHATO	IRWIN	KENT	3	7	3.65	382	90	30
2019	161	211	4	CHATO	IRWIN	KENT	3	8	4.00	475	90	30
2019	162	211	4	CHATO	IRWIN	KENT	3	9	4.05	393	90	60
2019	163	141	1	CHULUCANAS	CHULUCANAS	KENT	3	1	4.15	470	90	70
2019	164	141	1	CHULUCANAS	CHULUCANAS	KENT	3	2	4.20	287	80	80
2019	165	141	1	CHULUCANAS	CHULUCANAS	KENT	3	3	4.15	414	80	80
2019	166	141	1	CHULUCANAS	CHULUCANAS	KENT	3	4	3.16	497	75	80
2019	167	141	1	CHULUCANAS	CHULUCANAS	KENT	3	5	3.85	279	90	75
2019	168	141	1	CHULUCANAS	CHULUCANAS	KENT	3	6	3.40	179	50	75
2019	169	141	1	CHULUCANAS	CHULUCANAS	KENT	3	7	3.40	208	95	20
2019	170	141	1	CHULUCANAS	CHULUCANAS	KENT	3	8	3.20	256	90	70
2019	171	141	1	CHULUCANAS	CHULUCANAS	KENT	3	9	2.80	104	80	75
2019	172	231	2	CHATO	JULIE	KENT	3	1	4.45	539	90	30
2019	173	231	2	CHATO	JULIE	KENT	3	2	3.70	402	90	20
2019	174	231	2	CHATO	JULIE	KENT	3	3	3.85	310	90	20
2019	175	231	2	CHATO	JULIE	KENT	3	4	3.30	103	90	50
2019	176	231	2	CHATO	JULIE	KENT	3	5	3.40	50	60	50
2019	177	231	2	CHATO	JULIE	KENT	3	6	3.40	200	80	10
2019	178	231	2	CHATO	JULIE	KENT	3	7	3.95	324	90	75
2019	179	231	2	CHATO	JULIE	KENT	3	8	4.05	354	90	75
2019	180	231	2	CHATO	JULIE	KENT	3	9	3.25	135	70	70
2019	181	111	6	CHULUCANAS	IRWIN	KENT	3	1	2.80	130	80	70
2019	182	111	6	CHULUCANAS	IRWIN	KENT	3	2	3.00	140	80	40
2019	183	111	6	CHULUCANAS	IRWIN	KENT	3	3	3.65	284	80	80
2019	184	111	6	CHULUCANAS	IRWIN	KENT	3	4	3.65	261	90	80
2019	185	111	6	CHULUCANAS	IRWIN	KENT	3	5	3.75	220	95	20
2019	186	111	6	CHULUCANAS	IRWIN	KENT	3	6	3.15	175	70	80
2019	187	111	6	CHULUCANAS	IRWIN	KENT	3	7	3.60	310	95	70
2019	188	111	6	CHULUCANAS	IRWIN	KENT	3	8	3.60	440	90	70
2019	189	111	6	CHULUCANAS	IRWIN	KENT	3	9	4.20	415	90	70
2019	190	221	7	CHATO	CHATO	KENT	3	1	3.70	360	50	70
2019	191	221	7	CHATO	CHATO	KENT	3	2	4.70	230	90	15
2019	192	221	7	CHATO	CHATO	KENT	3	3	4.10	350	90	20
2019	193	221	7	CHATO	CHATO	KENT	3	4	4.00	423	90	70
2019	194	221	7	CHATO	CHATO	KENT	3	5	3.50	304	90	60
2019	195	221	7	CHATO	CHATO	KENT	3	6
2019	196	221	7	CHATO	CHATO	KENT	3	7	4.00	410	90	80
2019	197	221	7	CHATO	CHATO	KENT	3	8	3.90	380	90	80
2019	198	221	7	CHATO	CHATO	KENT	3	9	4.05	340	90	50
2019	199	131	5	CHULUCANAS	JULIE	KENT	3	1
2019	200	131	5	CHULUCANAS	JULIE	KENT	3	2	3.70	268	80	80
2019	201	131	5	CHULUCANAS	JULIE	KENT	3	3	3.40	190	85	80
2019	202	131	5	CHULUCANAS	JULIE	KENT	3	4	3.75	214	80	80
2019	203	131	5	CHULUCANAS	JULIE	KENT	3	5	3.70	380	90	80
2019	204	131	5	CHULUCANAS	JULIE	KENT	3	6		214
2019	205	131	5	CHULUCANAS	JULIE	KENT	3	7	3.60	300	95	80
2019	206	131	5	CHULUCANAS	JULIE	KENT	3	8	3.40	325	90	60
2019	207	131	5	CHULUCANAS	JULIE	KENT	3	9	3.70	350	90	70
2019	208	241	3	CHULUCANAS	CHATO	KENT	3	1	3.50	180	80	75
2019	209	241	3	CHULUCANAS	CHATO	KENT	3	2	4.40	295	90	25
2019	210	241	3	CHULUCANAS	CHATO	KENT	3	3	3.60	232	70	50
2019	211	241	3	CHULUCANAS	CHATO	KENT	3	4	4.05	250	70	80
2019	212	241	3	CHULUCANAS	CHATO	KENT	3	5	4.10	340	90	80
2019	213	241	3	CHULUCANAS	CHATO	KENT	3	6	4.05	382	80	80
2019	214	241	3	CHULUCANAS	CHATO	KENT	3	7	3.70	379	90	70
2019	215	241	3	CHULUCANAS	CHATO	KENT	3	8	3.50	120	80	70
2019	216	241	3	CHULUCANAS	CHATO	KENT	3	9	3.65	260	90	70

fru %>% kable(caption = "Evaluation of mango fruit quality", align = 'c')

Evaluation of mango fruit quality

year	n	treat	n_treat	stock	edge	scion	block	n_plant	sample	n_fruits	weigth	long	diameter_1	diameter_2	diameter_average
2023	1	141	1	CHULUCANAS	CHULUCANAS	KENT	1	1	1	26	270	86	78	74	76.0
2023	2	141	1	CHULUCANAS	CHULUCANAS	KENT	1	1	2	26	520	111	90	83	86.5
2023	3	141	1	CHULUCANAS	CHULUCANAS	KENT	1	1	3	26	385	103	86	75	80.5
2023	4	141	1	CHULUCANAS	CHULUCANAS	KENT	1	1	4	26	230	86	70	69	69.5
2023	5	141	1	CHULUCANAS	CHULUCANAS	KENT	1	1	5	26	285	88	78	71	74.5
2023	6	141	1	CHULUCANAS	CHULUCANAS	KENT	1	2	1	35	600	116	96	87	91.5
2023	7	141	1	CHULUCANAS	CHULUCANAS	KENT	1	2	2	35	457	101	88	88	88.0
2023	8	141	1	CHULUCANAS	CHULUCANAS	KENT	1	2	3	35	520	115	88	81	84.5
2023	9	141	1	CHULUCANAS	CHULUCANAS	KENT	1	2	4	35	665	126	103	95	99.0
2023	10	141	1	CHULUCANAS	CHULUCANAS	KENT	1	2	5	35	305	91	78	80	79.0
2023	11	231	2	CHATO	JULIE	KENT	1	1	1	118	422	100	91	83	87.0
2023	12	231	2	CHATO	JULIE	KENT	1	1	2	118	377	102	84	76	80.0
2023	13	231	2	CHATO	JULIE	KENT	1	1	3	118	300	91	84	72	78.0
2023	14	231	2	CHATO	JULIE	KENT	1	1	4	118	455	106	88	82	85.0
2023	15	231	2	CHATO	JULIE	KENT	1	1	5	118	485	108	91	83	87.0
2023	16	231	2	CHATO	JULIE	KENT	1	2	1	102	420	104	87	83	85.0
2023	17	231	2	CHATO	JULIE	KENT	1	2	2	102	395	99	86	80	83.0
2023	18	231	2	CHATO	JULIE	KENT	1	2	3	102	335	98	87	70	78.5
2023	19	231	2	CHATO	JULIE	KENT	1	2	4	102	610	118	99	92	95.5
2023	20	231	2	CHATO	JULIE	KENT	1	2	5	102	530	109	98	90	94.0
2023	21	241	3	CHULUCANAS	CHATO	KENT	1	1	1	86	485	105	90	83	86.5
2023	22	241	3	CHULUCANAS	CHATO	KENT	1	1	2	86	460	103	89	80	84.5
2023	23	241	3	CHULUCANAS	CHATO	KENT	1	1	3	86	576	111	101	86	93.5
2023	24	241	3	CHULUCANAS	CHATO	KENT	1	1	4	86	610	114	97	91	94.0
2023	25	241	3	CHULUCANAS	CHATO	KENT	1	1	5	86	505	111	92	85	88.5
2023	26	241	3	CHULUCANAS	CHATO	KENT	1	2	1	105	475	109	88	84	86.0
2023	27	241	3	CHULUCANAS	CHATO	KENT	1	2	2	105	350	95	83	77	80.0
2023	28	241	3	CHULUCANAS	CHATO	KENT	1	2	3	105	345	96	86	74	80.0
2023	29	241	3	CHULUCANAS	CHATO	KENT	1	2	4	105	475	101	95	86	90.5
2023	30	241	3	CHULUCANAS	CHATO	KENT	1	2	5	105	490	111	97	83	90.0
2023	31	211	4	CHATO	IRWIN	KENT	1	1	1	16	560	104	99	90	94.5
2023	32	211	4	CHATO	IRWIN	KENT	1	1	2	16	381	96	83	82	82.5
2023	33	211	4	CHATO	IRWIN	KENT	1	1	3	16	556	108	95	89	92.0
2023	34	211	4	CHATO	IRWIN	KENT	1	1	4	16	432	100	89	82	85.5
2023	35	211	4	CHATO	IRWIN	KENT	1	1	5	16	475	103	89	81	85.0
2023	36	211	4	CHATO	IRWIN	KENT	1	2	1	97	600	112	99	89	94.0
2023	37	211	4	CHATO	IRWIN	KENT	1	2	2	97	485	108	93	85	89.0
2023	38	211	4	CHATO	IRWIN	KENT	1	2	3	97	500	108	91	88	89.5
2023	39	211	4	CHATO	IRWIN	KENT	1	2	4	97	550	111	96	86	91.0
2023	40	211	4	CHATO	IRWIN	KENT	1	2	5	97	695	119	106	94	100.0
2023	41	131	5	CHULUCANAS	JULIE	KENT	1	1	1	94	367	100	82	80	81.0
2023	42	131	5	CHULUCANAS	JULIE	KENT	1	1	2	94	448	110	89	78	83.5
2023	43	131	5	CHULUCANAS	JULIE	KENT	1	1	3	94	400	104	87	77	82.0
2023	44	131	5	CHULUCANAS	JULIE	KENT	1	1	4	94	409	106	86	78	82.0
2023	45	131	5	CHULUCANAS	JULIE	KENT	1	1	5	94	402	102	90	80	85.0
2023	46	131	5	CHULUCANAS	JULIE	KENT	1	2	1	118	460	107	86	80	83.0
2023	47	131	5	CHULUCANAS	JULIE	KENT	1	2	2	118	450	107	94	81	87.5
2023	48	131	5	CHULUCANAS	JULIE	KENT	1	2	3	118	416	108	82	77	79.5
2023	49	131	5	CHULUCANAS	JULIE	KENT	1	2	4	118	500	107	97	85	91.0
2023	50	131	5	CHULUCANAS	JULIE	KENT	1	2	5	118	465	112	93	82	87.5
2023	51	111	6	CHULUCANAS	IRWIN	KENT	1	1	1	91	605	120	94	91	92.5
2023	52	111	6	CHULUCANAS	IRWIN	KENT	1	1	2	91	585	122	93	86	89.5
2023	53	111	6	CHULUCANAS	IRWIN	KENT	1	1	3	91	415	106	85	79	82.0
2023	54	111	6	CHULUCANAS	IRWIN	KENT	1	1	4	91	460	103	89	81	85.0
2023	55	111	6	CHULUCANAS	IRWIN	KENT	1	1	5	91	455	105	86	82	84.0
2023	56	111	6	CHULUCANAS	IRWIN	KENT	1	2	1	71	375	96	88	79	83.5
2023	57	111	6	CHULUCANAS	IRWIN	KENT	1	2	2	71	455	108	94	84	89.0
2023	58	111	6	CHULUCANAS	IRWIN	KENT	1	2	3	71	410	101	84	77	80.5
2023	59	111	6	CHULUCANAS	IRWIN	KENT	1	2	4	71	460	110	89	79	84.0
2023	60	111	6	CHULUCANAS	IRWIN	KENT	1	2	5	71	385	105	82	78	80.0
2023	61	221	7	CHATO	CHATO	KENT	1	1	1	13	440	100	90	81	85.5
2023	62	221	7	CHATO	CHATO	KENT	1	1	2	13	450	104	90	84	87.0
2023	63	221	7	CHATO	CHATO	KENT	1	1	3	13	540	110	91	88	89.5
2023	64	221	7	CHATO	CHATO	KENT	1	1	4	13	368	97	82	81	81.5
2023	65	221	7	CHATO	CHATO	KENT	1	1	5	13	341	93	85	78	81.5
2023	66	221	7	CHATO	CHATO	KENT	1	2	1	31	500	117	92	79	85.5
2023	67	221	7	CHATO	CHATO	KENT	1	2	2	31	305	96	81	71	76.0
2023	68	221	7	CHATO	CHATO	KENT	1	2	3	31	425	103	89	82	85.5
2023	69	221	7	CHATO	CHATO	KENT	1	2	4	31	360	101	82	78	80.0
2023	70	221	7	CHATO	CHATO	KENT	1	2	5	31	380	101	84	78	81.0
2023	71	121	8	CHATO	CHULUCANAS	KENT	1	1	1	29	340	95	80	77	78.5
2023	72	121	8	CHATO	CHULUCANAS	KENT	1	1	2	29	390	100	82	77	79.5
2023	73	121	8	CHATO	CHULUCANAS	KENT	1	1	3	29	480	110	92	81	86.5
2023	74	121	8	CHATO	CHULUCANAS	KENT	1	1	4	29	440	102	87	80	83.5
2023	75	121	8	CHATO	CHULUCANAS	KENT	1	1	5	29	450	104	90	83	86.5
2023	76	121	8	CHATO	CHULUCANAS	KENT	1	2	1	62	371	100	83	78	80.5
2023	77	121	8	CHATO	CHULUCANAS	KENT	1	2	2	62	381	95	89	81	85.0
2023	78	121	8	CHATO	CHULUCANAS	KENT	1	2	3	62	380	102	87	79	83.0
2023	79	121	8	CHATO	CHULUCANAS	KENT	1	2	4	62	452	105	91	84	87.5
2023	80	121	8	CHATO	CHULUCANAS	KENT	1	2	5	62	455	104	90	84	87.0
2023	81	141	1	CHULUCANAS	CHULUCANAS	KENT	2	1	1	51	385	98	90	79	84.5
2023	82	141	1	CHULUCANAS	CHULUCANAS	KENT	2	1	2	51	610	119	96	87	91.5
2023	83	141	1	CHULUCANAS	CHULUCANAS	KENT	2	1	3	51	640	120	100	86	93.0
2023	84	141	1	CHULUCANAS	CHULUCANAS	KENT	2	1	4	51	430	103	92	80	86.0
2023	85	141	1	CHULUCANAS	CHULUCANAS	KENT	2	1	5	51	500	107	95	87	91.0
2023	86	141	1	CHULUCANAS	CHULUCANAS	KENT	2	2	1	38	630	118	101	91	96.0
2023	87	141	1	CHULUCANAS	CHULUCANAS	KENT	2	2	2	38	512	107	97	86	91.5
2023	88	141	1	CHULUCANAS	CHULUCANAS	KENT	2	2	3	38	652	126	102	83	92.5
2023	89	141	1	CHULUCANAS	CHULUCANAS	KENT	2	2	4	38	510	108	93	88	90.5
2023	90	141	1	CHULUCANAS	CHULUCANAS	KENT	2	2	5	38	495	110	95	84	89.5
2023	91	231	2	CHATO	JULIE	KENT	2	1	1	57	441	102	93	82	87.5
2023	92	231	2	CHATO	JULIE	KENT	2	1	2	57	471	105	88	83	85.5
2023	93	231	2	CHATO	JULIE	KENT	2	1	3	57	645	120	100	95	97.5
2023	94	231	2	CHATO	JULIE	KENT	2	1	4	57	525	114	90	85	87.5
2023	95	231	2	CHATO	JULIE	KENT	2	1	5	57	360	101	89	72	80.5
2023	96	231	2	CHATO	JULIE	KENT	2	2	1	116	555	115	99	87	93.0
2023	97	231	2	CHATO	JULIE	KENT	2	2	2	116	495	116	91	80	85.5
2023	98	231	2	CHATO	JULIE	KENT	2	2	3	116	512	114	93	84	88.5
2023	99	231	2	CHATO	JULIE	KENT	2	2	4	116	605	119	95	85	90.0
2023	100	231	2	CHATO	JULIE	KENT	2	2	5	116	520	109	99	87	93.0
2023	101	241	3	CHULUCANAS	CHATO	KENT	2	1	1	91	425	102	91	82	86.5
2023	102	241	3	CHULUCANAS	CHATO	KENT	2	1	2	91	600	118	101	88	94.5
2023	103	241	3	CHULUCANAS	CHATO	KENT	2	1	3	91	765	119	107	94	100.5
2023	104	241	3	CHULUCANAS	CHATO	KENT	2	1	4	91	426	108	87	76	81.5
2023	105	241	3	CHULUCANAS	CHATO	KENT	2	1	5	91	475	106	97	84	90.5
2023	106	241	3	CHULUCANAS	CHATO	KENT	2	2	1	94	545	109	95	86	90.5
2023	107	241	3	CHULUCANAS	CHATO	KENT	2	2	2	94	371	100	85	78	81.5
2023	108	241	3	CHULUCANAS	CHATO	KENT	2	2	3	94	470	102	89	85	87.0
2023	109	241	3	CHULUCANAS	CHATO	KENT	2	2	4	94	445	105	94	83	88.5
2023	110	241	3	CHULUCANAS	CHATO	KENT	2	2	5	94	575	115	96	85	90.5
2023	111	211	4	CHATO	IRWIN	KENT	2	1	1	123	489	110	95	83	89.0
2023	112	211	4	CHATO	IRWIN	KENT	2	1	2	123	370	99	88	78	83.0
2023	113	211	4	CHATO	IRWIN	KENT	2	1	3	123	472	108	93	82	87.5
2023	114	211	4	CHATO	IRWIN	KENT	2	1	4	123	460	108	91	78	84.5
2023	115	211	4	CHATO	IRWIN	KENT	2	1	5	123	391	103	89	77	83.0
2023	116	211	4	CHATO	IRWIN	KENT	2	2	1	89	450	106	88	80	84.0
2023	117	211	4	CHATO	IRWIN	KENT	2	2	2	89	535	107	98	89	93.5
2023	118	211	4	CHATO	IRWIN	KENT	2	2	3	89	410	104	87	75	81.0
2023	119	211	4	CHATO	IRWIN	KENT	2	2	4	89	490	106	95	83	89.0
2023	120	211	4	CHATO	IRWIN	KENT	2	2	5	89	376	98	88	74	81.0
2023	121	131	5	CHULUCANAS	JULIE	KENT	2	1	1	109	385	98	89	79	84.0
2023	122	131	5	CHULUCANAS	JULIE	KENT	2	1	2	109	400	100	85	80	82.5
2023	123	131	5	CHULUCANAS	JULIE	KENT	2	1	3	109	635	120	100	88	94.0
2023	124	131	5	CHULUCANAS	JULIE	KENT	2	1	4	109	616	123	93	81	87.0
2023	125	131	5	CHULUCANAS	JULIE	KENT	2	1	5	109	445	108	89	78	83.5
2023	126	131	5	CHULUCANAS	JULIE	KENT	2	2	1	63	585	120	96	81	88.5
2023	127	131	5	CHULUCANAS	JULIE	KENT	2	2	2	63	388	99	88	81	84.5
2023	128	131	5	CHULUCANAS	JULIE	KENT	2	2	3	63	717	129	106	87	96.5
2023	129	131	5	CHULUCANAS	JULIE	KENT	2	2	4	63	478	107	95	83	89.0
2023	130	131	5	CHULUCANAS	JULIE	KENT	2	2	5	63	445	108	89	78	83.5
2023	131	111	6	CHULUCANAS	IRWIN	KENT	2	1	1	126	460	101	94	83	88.5
2023	132	111	6	CHULUCANAS	IRWIN	KENT	2	1	2	126	440	99	89	86	87.5
2023	133	111	6	CHULUCANAS	IRWIN	KENT	2	1	3	126	535	112	96	86	91.0
2023	134	111	6	CHULUCANAS	IRWIN	KENT	2	1	4	126	560	118	95	87	91.0
2023	135	111	6	CHULUCANAS	IRWIN	KENT	2	1	5	126	420	106	84	78	81.0
2023	136	111	6	CHULUCANAS	IRWIN	KENT	2	2	1	56	380	100	84	77	80.5
2023	137	111	6	CHULUCANAS	IRWIN	KENT	2	2	2	56	368	98	85	75	80.0
2023	138	111	6	CHULUCANAS	IRWIN	KENT	2	2	3	56	500	114	92	84	88.0
2023	139	111	6	CHULUCANAS	IRWIN	KENT	2	2	4	56	310	92	80	75	77.5
2023	140	111	6	CHULUCANAS	IRWIN	KENT	2	2	5	56	400	100	84	79	81.5
2023	141	221	7	CHATO	CHATO	KENT	2	1	1	103	405	100	89	79	84.0
2023	142	221	7	CHATO	CHATO	KENT	2	1	2	103	460	107	90	83	86.5
2023	143	221	7	CHATO	CHATO	KENT	2	1	3	103	470	107	90	81	85.5
2023	144	221	7	CHATO	CHATO	KENT	2	1	4	103	410	105	88	74	81.0
2023	145	221	7	CHATO	CHATO	KENT	2	1	5	103	610	115	103	92	97.5
2023	146	221	7	CHATO	CHATO	KENT	2	2	1	124	470	111	92	79	85.5
2023	147	221	7	CHATO	CHATO	KENT	2	2	2	124	435	109	86	79	82.5
2023	148	221	7	CHATO	CHATO	KENT	2	2	3	124	445	107	85	80	82.5
2023	149	221	7	CHATO	CHATO	KENT	2	2	4	124	430	104	91	79	85.0
2023	150	221	7	CHATO	CHATO	KENT	2	2	5	124	639	115	99	90	94.5
2023	151	121	8	CHATO	CHULUCANAS	KENT	2	1	1	95	615	119	98	89	93.5
2023	152	121	8	CHATO	CHULUCANAS	KENT	2	1	2	95	430	103	92	82	87.0
2023	153	121	8	CHATO	CHULUCANAS	KENT	2	1	3	95	460	104	89	84	86.5
2023	154	121	8	CHATO	CHULUCANAS	KENT	2	1	4	95	391	94	92	82	87.0
2023	155	121	8	CHATO	CHULUCANAS	KENT	2	1	5	95	410	100	93	80	86.5
2023	156	121	8	CHATO	CHULUCANAS	KENT	2	2	1	79	489	109	87	83	85.0
2023	157	121	8	CHATO	CHULUCANAS	KENT	2	2	2	79	569	113	98	89	93.5
2023	158	121	8	CHATO	CHULUCANAS	KENT	2	2	3	79	566	116	95	84	89.5
2023	159	121	8	CHATO	CHULUCANAS	KENT	2	2	4	79	475	106	96	84	90.0
2023	160	121	8	CHATO	CHULUCANAS	KENT	2	2	5	79	490	110	91	82	86.5
2023	161	141	1	CHULUCANAS	CHULUCANAS	KENT	3	1	1	85	361	95	86	78	82.0
2023	162	141	1	CHULUCANAS	CHULUCANAS	KENT	3	1	2	85	460	105	93	81	87.0
2023	163	141	1	CHULUCANAS	CHULUCANAS	KENT	3	1	3	85	465	103	94	82	88.0
2023	164	141	1	CHULUCANAS	CHULUCANAS	KENT	3	1	4	85	450	108	84	80	82.0
2023	165	141	1	CHULUCANAS	CHULUCANAS	KENT	3	1	5	85	552	111	97	84	90.5
2023	166	141	1	CHULUCANAS	CHULUCANAS	KENT	3	2	1	113	351	96	84	77	80.5
2023	167	141	1	CHULUCANAS	CHULUCANAS	KENT	3	2	2	113	584	117	97	84	90.5
2023	168	141	1	CHULUCANAS	CHULUCANAS	KENT	3	2	3	113	440	109	89	77	83.0
2023	169	141	1	CHULUCANAS	CHULUCANAS	KENT	3	2	4	113	420	103	90	80	85.0
2023	170	141	1	CHULUCANAS	CHULUCANAS	KENT	3	2	5	113	361	100	86	72	79.0
2023	171	231	2	CHATO	JULIE	KENT	3	1	1	57	645	120	102	89	95.5
2023	172	231	2	CHATO	JULIE	KENT	3	1	2	57	440	105	83	79	81.0
2023	173	231	2	CHATO	JULIE	KENT	3	1	3	57	300	97	81	70	75.5
2023	174	231	2	CHATO	JULIE	KENT	3	1	4	57	495	113	96	81	88.5
2023	175	231	2	CHATO	JULIE	KENT	3	1	5	57	460	111	88	78	83.0
2023	176	231	2	CHATO	JULIE	KENT	3	2	1	53	360	102	89	72	80.5
2023	177	231	2	CHATO	JULIE	KENT	3	2	2	53	444	108	86	82	84.0
2023	178	231	2	CHATO	JULIE	KENT	3	2	3	53	489	112	91	83	87.0
2023	179	231	2	CHATO	JULIE	KENT	3	2	4	53	488	111	93	82	87.5
2023	180	231	2	CHATO	JULIE	KENT	3	2	5	53	400	101	87	79	83.0
2023	181	241	3	CHULUCANAS	CHATO	KENT	3	1	1	87	655	123	101	91	96.0
2023	182	241	3	CHULUCANAS	CHATO	KENT	3	1	2	87	392	103	88	78	83.0
2023	183	241	3	CHULUCANAS	CHATO	KENT	3	1	3	87	316	96	83	73	78.0
2023	184	241	3	CHULUCANAS	CHATO	KENT	3	1	4	87	420	101	86	83	84.5
2023	185	241	3	CHULUCANAS	CHATO	KENT	3	1	5	87	410	103	87	79	83.0
2023	186	241	3	CHULUCANAS	CHATO	KENT	3	2	1	64	490	108	88	82	85.0
2023	187	241	3	CHULUCANAS	CHATO	KENT	3	2	2	64	400	95	89	82	85.5
2023	188	241	3	CHULUCANAS	CHATO	KENT	3	2	3	64	575	112	96	89	92.5
2023	189	241	3	CHULUCANAS	CHATO	KENT	3	2	4	64	613	112	98	91	94.5
2023	190	241	3	CHULUCANAS	CHATO	KENT	3	2	5	64	410	103	88	80	84.0
2023	191	211	4	CHATO	IRWIN	KENT	3	1	1	92	530	113	99	88	93.5
2023	192	211	4	CHATO	IRWIN	KENT	3	1	2	92	391	101	90	77	83.5
2023	193	211	4	CHATO	IRWIN	KENT	3	1	3	92	418	102	89	77	83.0
2023	194	211	4	CHATO	IRWIN	KENT	3	1	4	92	450	100	87	83	85.0
2023	195	211	4	CHATO	IRWIN	KENT	3	1	5	92	550	112	97	85	91.0
2023	196	211	4	CHATO	IRWIN	KENT	3	2	1	96	510	112	93	80	86.5
2023	197	211	4	CHATO	IRWIN	KENT	3	2	2	96	700	124	102	90	96.0
2023	198	211	4	CHATO	IRWIN	KENT	3	2	3	96	470	104	89	87	88.0
2023	199	211	4	CHATO	IRWIN	KENT	3	2	4	96	350	95	82	77	79.5
2023	200	211	4	CHATO	IRWIN	KENT	3	2	5	96	415	101	89	80	84.5
2023	201	131	5	CHULUCANAS	JULIE	KENT	3	1	1	64	450	108	91	78	84.5
2023	202	131	5	CHULUCANAS	JULIE	KENT	3	1	2	64	420	109	90	75	82.5
2023	203	131	5	CHULUCANAS	JULIE	KENT	3	1	3	64	640	120	104	90	97.0
2023	204	131	5	CHULUCANAS	JULIE	KENT	3	1	4	64	520	111	91	83	87.0
2023	205	131	5	CHULUCANAS	JULIE	KENT	3	1	5	64	625	126	95	86	90.5
2023	206	131	5	CHULUCANAS	JULIE	KENT	3	2	1	162	490	110	89	82	85.5
2023	207	131	5	CHULUCANAS	JULIE	KENT	3	2	2	162	515	107	92	84	88.0
2023	208	131	5	CHULUCANAS	JULIE	KENT	3	2	3	162	390	103	85	75	80.0
2023	209	131	5	CHULUCANAS	JULIE	KENT	3	2	4	162	465	105	89	80	84.5
2023	210	131	5	CHULUCANAS	JULIE	KENT	3	2	5	162	615	118	100	89	94.5
2023	211	111	6	CHULUCANAS	IRWIN	KENT	3	1	1	128	465	105	90	80	85.0
2023	212	111	6	CHULUCANAS	IRWIN	KENT	3	1	2	128	535	116	93	85	89.0
2023	213	111	6	CHULUCANAS	IRWIN	KENT	3	1	3	128	375	100	85	76	80.5
2023	214	111	6	CHULUCANAS	IRWIN	KENT	3	1	4	128	410	103	86	77	81.5
2023	215	111	6	CHULUCANAS	IRWIN	KENT	3	1	5	128	435	109	87	81	84.0
2023	216	111	6	CHULUCANAS	IRWIN	KENT	3	2	1	93	452	108	88	81	84.5
2023	217	111	6	CHULUCANAS	IRWIN	KENT	3	2	2	93	540	117	92	84	88.0
2023	218	111	6	CHULUCANAS	IRWIN	KENT	3	2	3	93	426	105	85	79	82.0
2023	219	111	6	CHULUCANAS	IRWIN	KENT	3	2	4	93	366	106	84	75	79.5
2023	220	111	6	CHULUCANAS	IRWIN	KENT	3	2	5	93	435	108	93	78	85.5
2023	221	221	7	CHATO	CHATO	KENT	3	1	1	113	615	120	101	85	93.0
2023	222	221	7	CHATO	CHATO	KENT	3	1	2	113	505	113	90	80	85.0
2023	223	221	7	CHATO	CHATO	KENT	3	1	3	113	430	107	86	78	82.0
2023	224	221	7	CHATO	CHATO	KENT	3	1	4	113	475	106	92	83	87.5
2023	225	221	7	CHATO	CHATO	KENT	3	1	5	113	325	93	81	72	76.5
2023	226	221	7	CHATO	CHATO	KENT	3	2	1	99	470	106	89	82	85.5
2023	227	221	7	CHATO	CHATO	KENT	3	2	2	99	480	109	96	80	88.0
2023	228	221	7	CHATO	CHATO	KENT	3	2	3	99	525	109	97	85	91.0
2023	229	221	7	CHATO	CHATO	KENT	3	2	4	99	452	113	94	77	85.5
2023	230	221	7	CHATO	CHATO	KENT	3	2	5	99	416	101	89	82	85.5
2023	231	121	8	CHATO	CHULUCANAS	KENT	3	1	1	103	470	108	90	81	85.5
2023	232	121	8	CHATO	CHULUCANAS	KENT	3	1	2	103	410	105	88	76	82.0
2023	233	121	8	CHATO	CHULUCANAS	KENT	3	1	3	103	450	111	89	81	85.0
2023	234	121	8	CHATO	CHULUCANAS	KENT	3	1	4	103	620	119	100	86	93.0
2023	235	121	8	CHATO	CHULUCANAS	KENT	3	1	5	103
2023	236	121	8	CHATO	CHULUCANAS	KENT	3	2	1	108	471	111	90	80	85.0
2023	237	121	8	CHATO	CHULUCANAS	KENT	3	2	2	108	475	110	93	81	87.0
2023	238	121	8	CHATO	CHULUCANAS	KENT	3	2	3	108	525	114	91	81	86.0
2023	239	121	8	CHATO	CHULUCANAS	KENT	3	2	4	108	426	100	86	82	84.0
2023	240	121	8	CHATO	CHULUCANAS	KENT	3	2	5	108	527	113	88	85	86.5

3 Data summary

Summary of the number of data points recorded for each treatment and evaluated variable.

sm <- rdt %>% 
  group_by(year, treat) %>% 
  summarise(across(height:sproud, ~ sum(!is.na(.))))

sm
## # A tibble: 24 × 6
## # Groups:   year [3]
##    year  treat height n_fruits flowering sproud
##    <fct> <fct>  <int>    <int>     <int>  <int>
##  1 2017  111        0       26        26      0
##  2 2017  121        0       26        26      0
##  3 2017  131        0       25        25      0
##  4 2017  141        0       26        26      0
##  5 2017  211        0       26        26      0
##  6 2017  221        0       26        26      0
##  7 2017  231        0       26        26      0
##  8 2017  241        0       27        27      0
##  9 2018  111        0        9        22      0
## 10 2018  121        0        9        23      0
## # ℹ 14 more rows

sm <- fru %>% 
  group_by(year, treat) %>% 
  summarise(across(weigth:diameter_average, ~ sum(!is.na(.))))

sm
## # A tibble: 8 × 7
## # Groups:   year [1]
##   year  treat weigth  long diameter_1 diameter_2 diameter_average
##   <fct> <fct>  <int> <int>      <int>      <int>            <int>
## 1 2023  111       30    30         30         30               30
## 2 2023  121       29    29         29         29               29
## 3 2023  131       30    30         30         30               30
## 4 2023  141       30    30         30         30               30
## 5 2023  211       30    30         30         30               30
## 6 2023  221       30    30         30         30               30
## 7 2023  231       30    30         30         30               30
## 8 2023  241       30    30         30         30               30

4 Meteorological data

Climatic conditions of the study area located in the Tambogrande district, Piura region.

met <- range_read(ss = gs, sheet = "clima") %>% 
  mutate(date = as_date(Fecha))

scale <- 2

plot <- met %>% 
  ggplot(aes(x = date)) +
  geom_line(aes(y = TMax, color = "Tmax (°C)"), size= 0.8, linetype = "longdash") + 
  geom_line(aes(y = TMin, color = "Tmin (°C)"), size= 0.8, linetype = "dotted") +
  geom_bar(aes(y = PP/scale)
            , stat="identity", size=.1, fill="blue", color="black", alpha=.4) +
  geom_line(aes(y = HR/scale, color = "HR (%)"), size = 0.8, linetype = "twodash") +
  scale_color_manual("", values = c("skyblue", "red", "blue")) +
  scale_y_continuous(limits = c(0, 50)
                     , expand = c(0, 0)
                     , name = "Temperature (°C)"
                     , sec.axis = sec_axis(~ . * scale, name = "Precipitation (mm)")
                     ) +
  scale_x_date(date_breaks = "3 month", date_labels = "%b-%Y", name = NULL) +
  theme_minimal_grid() +
  theme(legend.position = "top") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

plot %>% 
  ggsave2(plot = ., "submission/Figure_2.jpg", units = "cm"
         , width = 25, height = 15)

plot %>% 
  ggsave2(plot = ., "submission/Figure_2.eps", units = "cm"
         , width = 25, height = 15)

knitr::include_graphics("submission/Figure_2.jpg")

5 Objetives

Evaluate the effect of the rootstock-interstock interaction on the agronomic traits and fruit biometrics of the mango crop in the San Lorenzo Valley.

5.1 Specific Objective 1

Determine the effect of the rootstock-interstock interaction on the agronomic characteristics of mango.

5.1.1 Plant height

trait <- "height"

lmm <- paste({{trait}}, "~ 1 + (1|block) + stock*edge") %>% as.formula()

lmd <- paste({{trait}}, "~ block + stock*edge") %>% as.formula()

rmout <- rdt %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot

rmout$outliers
##     index block      stock  edge height      resi   res_MAD   rawp.BHStud
## 87    523     2      CHATO JULIE   2.00 -1.643199 -3.984232 0.00006769863
## 126   563     2 CHULUCANAS CHATO   1.90 -1.731293 -4.197830 0.00002694848
## 127   564     2 CHULUCANAS CHATO   1.85 -1.781293 -4.319064 0.00001566924
##              adjp       bholm out_flag
## 87  0.00006769863 0.013810521  OUTLIER
## 126 0.00002694848 0.005524438  OUTLIER
## 127 0.00001566924 0.003227863  OUTLIER

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: height
##             Df Sum Sq Mean Sq F value   Pr(>F)    
## block        2  3.140 1.57012  9.1832 0.000155 ***
## stock        1  1.273 1.27268  7.4435 0.006954 ** 
## edge         3  1.994 0.66450  3.8865 0.009977 ** 
## stock:edge   3  2.301 0.76689  4.4853 0.004549 ** 
## Residuals  193 32.999 0.17098                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ edge|stock) %>%
  cld(Letters = letters, reversed = F) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()

	edge	stock	emmean	SE	df	lower.CL	upper.CL	group
2	CHULUCANAS	CHATO	3.662963	0.0795773	193	3.506010	3.819916	a
1	CHATO	CHATO	3.741984	0.0811085	193	3.582011	3.901957	a
4	JULIE	CHATO	3.860337	0.0827154	193	3.697194	4.023479	a
3	IRWIN	CHATO	3.915061	0.0811085	193	3.755088	4.075034	a
8	JULIE	CHULUCANAS	3.467553	0.0844563	193	3.300977	3.634129	a
6	CHULUCANAS	CHULUCANAS	3.497320	0.0811085	193	3.337347	3.657293	a
7	IRWIN	CHULUCANAS	3.649114	0.0844563	193	3.482538	3.815690	ab
5	CHATO	CHULUCANAS	3.925048	0.0827647	193	3.761808	4.088287	b

p1a <- mc %>% 
  plot_smr(x = "stock"
           , y = "emmean"
           , group = "edge"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Rootstock"
           , ylab = "Plant height (m)"
           , glab = "Interstock"
           , ylimits = c(0, 6, 2)
           , 
           )

p1a

5.1.2 Sproud

trait <- "sproud"

lmm <- paste({{trait}}, "~ 1 + (1|block) + stock*edge") %>% as.formula()

lmd <- paste({{trait}}, "~ block + stock*edge") %>% as.formula()

rmout <- rdt %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot

rmout$outliers
##     index block      stock       edge sproud      resi   res_MAD  rawp.BHStud
## 8     441     1 CHULUCANAS CHULUCANAS      0 -63.26923 -4.267451 0.0000197719
## 126   567     2 CHULUCANAS      CHATO     10 -58.26923 -3.930206 0.0000848732
##             adjp       bholm out_flag
## 8   0.0000197719 0.003974153  OUTLIER
## 126 0.0000848732 0.016974639  OUTLIER

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: sproud
##             Df Sum Sq Mean Sq F value     Pr(>F)    
## block        2    362   180.9  0.5152    0.59820    
## stock        1   6579  6578.9 18.7416 0.00002425 ***
## edge         3   2054   684.7  1.9506    0.12292    
## stock:edge   3   2713   904.2  2.5758    0.05521 .  
## Residuals  189  66345   351.0                       
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ edge|stock) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()

	edge	stock	emmean	SE	df	lower.CL	upper.CL	group
4	CHULUCANAS	CHATO	65.17927	3.675132	189	57.92972	72.42882	a
3	CHATO	CHATO	54.81356	3.747943	189	47.42039	62.20674	a
1	IRWIN	CHATO	52.08471	3.750192	189	44.68710	59.48232	a
2	JULIE	CHATO	51.91004	3.675132	189	44.66049	59.15959	a
7	JULIE	CHULUCANAS	72.15745	3.826839	189	64.60864	79.70625	a
6	CHATO	CHULUCANAS	70.57288	3.750234	189	63.17519	77.97057	a
5	CHULUCANAS	CHULUCANAS	65.94241	3.750188	189	58.54481	73.34001	a
8	IRWIN	CHULUCANAS	61.21227	3.910119	189	53.49919	68.92535	a

p1b <- mc %>% 
  plot_smr(x = "stock"
           , y = "emmean"
           , group = "edge"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Rootstock"
           , ylab = "Sproud ('%')"
           , glab = "Interstock"
           , ylimits = c(0, 100, 20)
           )

p1b 

5.1.3 Number of fruits

trait <- "n_fruits"

lmm <- paste({{trait}}, "~ 1 + (1|block) + year + stock*edge + (1 + year|treat)") %>% as.formula()

lmd <- paste({{trait}}, "~ block + year + stock*edge") %>% as.formula()

rmout <- rdt %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot

rmout$outliers
##     index block year stock  edge treat n_fruits     resi  res_MAD   rawp.BHStud
## 442   604     3 2019 CHATO JULIE   231      539 287.2486 4.008689 0.00006105685
##              adjp      bholm out_flag
## 442 0.00006105685 0.02955151  OUTLIER

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: n_fruits
##             Df  Sum Sq Mean Sq F value          Pr(>F)    
## block        2  261207  130604 21.6839 0.0000000009805 ***
## year         2  126248   63124 10.4804 0.0000352211768 ***
## stock        1    9326    9326  1.5483          0.2140    
## edge         3   22131    7377  1.2248          0.3001    
## stock:edge   3   15548    5183  0.8605          0.4615    
## Residuals  471 2836870    6023                            
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ year|edge|stock) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()

	year	edge	stock	emmean	SE	df	lower.CL	upper.CL	group
1	2018	CHATO	CHATO	197.2613	13.04649	471	171.6248	222.8978	a
3	2019	CHATO	CHATO	194.2890	10.75243	471	173.1603	215.4177	a
2	2017	CHATO	CHATO	162.5132	10.74838	471	141.3925	183.6339	b
13	2018	CHATO	CHULUCANAS	205.1341	12.95537	471	179.6767	230.5916	a
15	2019	CHATO	CHULUCANAS	202.1618	10.59847	471	181.3357	222.9880	a
14	2017	CHATO	CHULUCANAS	170.3860	10.59352	471	149.5696	191.2024	b
4	2018	CHULUCANAS	CHATO	206.5226	13.04648	471	180.8861	232.1591	a
6	2019	CHULUCANAS	CHATO	203.5503	10.75303	471	182.4205	224.6802	a
5	2017	CHULUCANAS	CHATO	171.7745	10.74778	471	150.6550	192.8940	b
16	2018	CHULUCANAS	CHULUCANAS	233.6210	13.04648	471	207.9845	259.2575	a
18	2019	CHULUCANAS	CHULUCANAS	230.6487	10.75303	471	209.5188	251.7785	a
17	2017	CHULUCANAS	CHULUCANAS	198.8729	10.74778	471	177.7533	219.9924	b
7	2018	IRWIN	CHATO	213.0482	13.04649	471	187.4117	238.6847	a
9	2019	IRWIN	CHATO	210.0759	10.75243	471	188.9472	231.2046	a
8	2017	IRWIN	CHATO	178.3001	10.74838	471	157.1794	199.4208	b
19	2018	IRWIN	CHULUCANAS	209.5332	13.19501	471	183.6048	235.4616	a
21	2019	IRWIN	CHULUCANAS	206.5609	11.07069	471	184.8068	228.3150	a
20	2017	IRWIN	CHULUCANAS	174.7851	10.92618	471	153.3150	196.2552	b
10	2018	JULIE	CHATO	207.7604	13.14195	471	181.9362	233.5845	a
12	2019	JULIE	CHATO	204.7881	10.95974	471	183.2520	226.3241	a
11	2017	JULIE	CHATO	173.0122	10.86339	471	151.6655	194.3589	b
22	2018	JULIE	CHULUCANAS	211.3414	13.14337	471	185.5145	237.1683	a
24	2019	JULIE	CHULUCANAS	208.3691	10.91480	471	186.9214	229.8169	a
23	2017	JULIE	CHULUCANAS	176.5933	10.91078	471	155.1535	198.0331	b

p1c <- mc %>% 
  plot_smr(type = "bar"
           , x = "year"
           , y = "emmean"
           , group = "edge"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Year"
           , ylab = "Fruits number"
           , glab = "Interstock"
           , ylimits = c(0, 320, 60)
           ) +
  facet_wrap(. ~ stock, nrow = 2)

p1c 

5.1.4 Flowering

trait <- "flowering"

lmm <- paste({{trait}}, "~ 1 + (1|block) + year + stock*edge + (1 + year|treat)") %>% as.formula()

lmd <- paste({{trait}}, "~ block + year + stock*edge") %>% as.formula()

rmout <- rdt %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot

rmout$outliers
##     index block year      stock       edge treat flowering      resi   res_MAD
## 28     31     1 2017      CHATO      IRWIN   211         5 -68.74456 -6.212394
## 32     35     1 2017      CHATO      IRWIN   211        25 -48.74456 -4.405009
## 33     36     1 2017      CHATO      IRWIN   211        25 -48.74456 -4.405009
## 64     67     1 2017      CHATO      CHATO   221        20 -56.92226 -5.144022
## 65     68     1 2017      CHATO      CHATO   221        30 -46.92226 -4.240330
## 67     70     1 2017      CHATO      CHATO   221        15 -61.92226 -5.595869
## 69     72     1 2017      CHATO      CHATO   221        15 -61.92226 -5.595869
## 127   131     2 2017 CHULUCANAS      CHATO   241        25 -58.44225 -5.281382
## 166   171     3 2017 CHULUCANAS CHULUCANAS   141        20 -58.28192 -5.266893
## 213   225     1 2018 CHULUCANAS CHULUCANAS   141        20 -57.68630 -5.213068
## 232   258     1 2018 CHULUCANAS      JULIE   131        10 -56.04249 -5.064518
## 243   273     1 2018 CHULUCANAS      IRWIN   111        15 -58.16121 -5.255986
## 250   284     1 2018      CHATO      CHATO   221        90  47.03144  4.250196
## 251   288     1 2018      CHATO      CHATO   221        97  54.03144  4.882781
## 254   292     2 2018      CHATO      IRWIN   211         5 -63.87075 -5.771952
## 289   328     2 2018      CHATO      CHATO   221         3 -43.83305 -3.961160
## 292   331     2 2018      CHATO      CHATO   221         0 -46.83305 -4.232268
## 298   338     2 2018 CHULUCANAS      IRWIN   111        10 -67.02571 -6.057063
## 299   339     2 2018 CHULUCANAS      IRWIN   111         5 -72.02571 -6.508909
## 311   352     2 2018 CHULUCANAS      JULIE   131        20 -49.90698 -4.510056
## 333   376     3 2018      CHATO      IRWIN   211        20 -44.36027 -4.008804
## 342   385     3 2018 CHULUCANAS CHULUCANAS   141         0 -77.04031 -6.962075
## 346   389     3 2018      CHATO      JULIE   231         0 -47.63760 -4.304974
## 347   390     3 2018      CHATO      JULIE   231         0 -47.63760 -4.304974
## 349   392     3 2018      CHATO      JULIE   231         2 -45.63760 -4.124236
## 375   421     3 2018 CHULUCANAS      JULIE   131        10 -55.39650 -5.006140
## 376   422     3 2018 CHULUCANAS      JULIE   131        20 -45.39650 -4.102448
## 454   505     2 2019      CHATO      IRWIN   211        30 -48.76953 -4.407266
## 526   581     3 2019      CHATO CHULUCANAS   121         7 -70.54614 -6.375202
##              rawp.BHStud                 adjp             bholm out_flag
## 28  0.000000000521833021 0.000000000521833021 0.000000305794150  OUTLIER
## 32  0.000010577939756562 0.000010577939756562 0.006040003600997  OUTLIER
## 33  0.000010577939756562 0.000010577939756562 0.006040003600997  OUTLIER
## 64  0.000000268917649482 0.000000268917649482 0.000155165483751  OUTLIER
## 65  0.000022319169655338 0.000022319169655338 0.012632650024921  OUTLIER
## 67  0.000000021952000884 0.000000021952000884 0.000012798016515  OUTLIER
## 69  0.000000021952000884 0.000000021952000884 0.000012798016515  OUTLIER
## 127 0.000000128212855666 0.000000128212855666 0.000074491669142  OUTLIER
## 166 0.000000138751758749 0.000000138751758749 0.000080476020075  OUTLIER
## 213 0.000000185743196379 0.000000185743196379 0.000107359567507  OUTLIER
## 232 0.000000409435427784 0.000000409435427784 0.000235834806404  OUTLIER
## 243 0.000000147233846226 0.000000147233846226 0.000085248396965  OUTLIER
## 250 0.000021358339519217 0.000021358339519217 0.012110178507396  OUTLIER
## 251 0.000001046000612570 0.000001046000612570 0.000600404351615  OUTLIER
## 254 0.000000007835861071 0.000000007835861071 0.000004576142866  OUTLIER
## 289 0.000074586480278027 0.000074586480278027 0.041843015435973  OUTLIER
## 292 0.000023134658141855 0.000023134658141855 0.013071081850148  OUTLIER
## 298 0.000000001386292858 0.000000001386292858 0.000000810981322  OUTLIER
## 299 0.000000000075698336 0.000000000075698336 0.000000044510622  OUTLIER
## 311 0.000006481038199890 0.000006481038199890 0.003713634888537  OUTLIER
## 333 0.000061026989556012 0.000061026989556012 0.034297168130479  OUTLIER
## 342 0.000000000003352874 0.000000000003352874 0.000000001974843  OUTLIER
## 346 0.000016700498484123 0.000016700498484123 0.009502583637466  OUTLIER
## 347 0.000016700498484123 0.000016700498484123 0.009502583637466  OUTLIER
## 349 0.000037196739853362 0.000037196739853362 0.020978961277296  OUTLIER
## 375 0.000000555322711371 0.000000555322711371 0.000319310559038  OUTLIER
## 376 0.000040880207851313 0.000040880207851313 0.023015557020289  OUTLIER
## 454 0.000010468378508710 0.000010468378508710 0.005987912506982  OUTLIER
## 526 0.000000000182721394 0.000000000182721394 0.000000107257458  OUTLIER

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: flowering
##             Df Sum Sq Mean Sq F value            Pr(>F)    
## block        2   1978   988.9  3.3541           0.03566 *  
## year         2  16473  8236.6 27.9371 0.000000000002796 ***
## stock        1    804   803.6  2.7257           0.09932 .  
## edge         3   7276  2425.3  8.2263 0.000023116374039 ***
## stock:edge   3   1580   526.5  1.7859           0.14874    
## Residuals  548 161564   294.8                              
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ year|edge|stock) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()

	year	edge	stock	emmean	SE	df	lower.CL	upper.CL	group
2	2019	CHATO	CHATO	77.01196	2.305001	548	72.48424	81.53968	a
1	2017	CHATO	CHATO	76.99530	2.345782	548	72.38748	81.60313	a
3	2018	CHATO	CHATO	64.90407	2.425731	548	60.13920	69.66894	b
14	2019	CHATO	CHULUCANAS	77.06506	2.198633	548	72.74628	81.38384	a
13	2017	CHATO	CHULUCANAS	77.04841	2.208166	548	72.71090	81.38591	a
15	2018	CHATO	CHULUCANAS	64.95718	2.269647	548	60.49890	69.41545	b
5	2019	CHULUCANAS	CHATO	87.26768	2.233767	548	82.87988	91.65547	a
4	2017	CHULUCANAS	CHATO	87.25102	2.226577	548	82.87735	91.62469	a
6	2018	CHULUCANAS	CHATO	75.15979	2.289284	548	70.66294	79.65663	b
17	2019	CHULUCANAS	CHULUCANAS	85.98612	2.243058	548	81.58008	90.39217	a
16	2017	CHULUCANAS	CHULUCANAS	85.96947	2.254128	548	81.54168	90.39726	a
18	2018	CHULUCANAS	CHULUCANAS	73.87824	2.329225	548	69.30293	78.45354	b
8	2019	IRWIN	CHATO	81.00814	2.291841	548	76.50627	85.51001	a
7	2017	IRWIN	CHATO	80.99148	2.311773	548	76.45046	85.53250	a
9	2018	IRWIN	CHATO	68.90025	2.375187	548	64.23467	73.56584	b
20	2019	IRWIN	CHULUCANAS	86.70568	2.312242	548	82.16374	91.24763	a
19	2017	IRWIN	CHULUCANAS	86.68903	2.284768	548	82.20105	91.17700	a
21	2018	IRWIN	CHULUCANAS	74.59780	2.389683	548	69.90374	79.29186	b
11	2019	JULIE	CHATO	78.22556	2.271401	548	73.76384	82.68728	a
10	2017	JULIE	CHATO	78.20890	2.263368	548	73.76296	82.65484	a
12	2018	JULIE	CHATO	66.11767	2.367724	548	61.46675	70.76860	b
23	2019	JULIE	CHULUCANAS	84.56367	2.325199	548	79.99628	89.13107	a
22	2017	JULIE	CHULUCANAS	84.54702	2.315901	548	79.99789	89.09615	a
24	2018	JULIE	CHULUCANAS	72.45579	2.429118	548	67.68427	77.22731	b

p1d <- mc %>% 
  plot_smr(type = "bar"
           , x = "year"
           , y = "emmean"
           , group = "edge"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Year"
           , ylab = "Flowering ('%')"
           , glab = "Interstock"
           , ylimits = c(0, 120, 20)
           ) +
  facet_wrap(. ~ stock, nrow = 2)

p1d

5.1.5 Figure 3

Univariate analysis of the variables that determine the agronomic characteristics of mango.

legend <- cowplot::get_plot_component(p1a, 'guide-box-top', return_all = TRUE)

p1 <- list(p1a + theme(legend.position="none")
           , p1b + theme(legend.position="none")
           , p1c + theme(legend.position="none")
           , p1d + theme(legend.position="none")
           ) %>% 
  plot_grid(plotlist = ., ncol = 2
            , labels = "auto"
            , rel_heights = c(1, 2)
            ) 

fig <- plot_grid(legend, p1, ncol = 1, align = 'v', rel_heights = c(0.05, 1))

fig %>% 
  ggsave2(plot = ., "submission/Figure_3.jpg"
         , units = "cm"
         , width = 24
         , height = 16
         )

fig %>% 
  ggsave2(plot = ., "submission/Figure_3.eps"
         , units = "cm"
         , width = 24
         , height = 16
         )

knitr::include_graphics("submission/Figure_3.jpg")

5.1.6 Multivariate

Principal Component Analysis (PCA) of agronomic traits in the mango crop based on the use of rootstock-interstock combinations.

mv <- rdt %>% 
  group_by(stock, edge) %>% 
  summarise(across(where(is.numeric), ~ mean(., na.rm = T))) %>%   
  unite("treat", stock:edge, sep = "-") %>% 
   rename(Treat = treat
         , Height = height
         , Fruits = n_fruits
         , Flowering = flowering
         , Sproud = sproud)
  
pca <- mv %>% 
  PCA(scale.unit = T, quali.sup = 1, graph = F) 

# summary

summary(pca, nbelements = Inf, nb.dec = 2)
## 
## Call:
## PCA(X = ., scale.unit = T, quali.sup = 1, graph = F) 
## 
## 
## Eigenvalues
##                       Dim.1  Dim.2  Dim.3  Dim.4
## Variance               2.49   0.93   0.37   0.21
## % of var.             62.27  23.26   9.22   5.26
## Cumulative % of var.  62.27  85.52  94.74 100.00
## 
## Individuals
##                          Dist   Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3
## 1                     |  2.28 | -1.99 19.84  0.76 | -0.72  7.04  0.10 | -0.43
## 2                     |  1.67 |  1.05  5.51  0.39 | -0.61  4.97  0.13 |  1.15
## 3                     |  2.18 | -1.90 18.19  0.76 |  0.89 10.71  0.17 |  0.36
## 4                     |  1.74 | -1.44 10.38  0.68 |  0.94 11.90  0.29 | -0.19
## 5                     |  1.52 | -0.49  1.23  0.11 | -1.05 14.72  0.47 | -0.41
## 6                     |  2.83 |  2.26 25.69  0.64 |  1.63 35.67  0.33 | -0.51
## 7                     |  1.00 |  0.68  2.34  0.46 | -0.03  0.01  0.00 |  0.67
## 8                     |  2.21 |  1.83 16.82  0.69 | -1.06 14.98  0.23 | -0.63
##                         ctr  cos2  
## 1                      6.36  0.04 |
## 2                     44.91  0.47 |
## 3                      4.40  0.03 |
## 4                      1.21  0.01 |
## 5                      5.78  0.07 |
## 6                      8.90  0.03 |
## 7                     15.00  0.44 |
## 8                     13.43  0.08 |
## 
## Variables
##                         Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3   ctr
## Height                | -0.91 33.26  0.83 |  0.07  0.53  0.00 |  0.25 17.31
## Fruits                |  0.57 13.26  0.33 |  0.78 66.19  0.62 | -0.16  6.81
## Flowering             |  0.86 29.71  0.74 |  0.05  0.23  0.00 |  0.51 69.79
## Sproud                |  0.77 23.77  0.59 | -0.55 33.05  0.31 | -0.15  6.10
##                        cos2  
## Height                 0.06 |
## Fruits                 0.03 |
## Flowering              0.26 |
## Sproud                 0.02 |
## 
## Supplementary categories
##                          Dist   Dim.1  cos2 v.test   Dim.2  cos2 v.test   Dim.3
## CHATO-CHATO           |  2.28 | -1.99  0.76  -1.26 | -0.72  0.10  -0.75 | -0.43
## CHATO-CHULUCANAS      |  1.67 |  1.05  0.39   0.66 | -0.61  0.13  -0.63 |  1.15
## CHATO-IRWIN           |  2.18 | -1.90  0.76  -1.21 |  0.89  0.17   0.93 |  0.36
## CHATO-JULIE           |  1.74 | -1.44  0.68  -0.91 |  0.94  0.29   0.98 | -0.19
## CHULUCANAS-CHATO      |  1.52 | -0.49  0.11  -0.31 | -1.05  0.47  -1.09 | -0.41
## CHULUCANAS-CHULUCANAS |  2.83 |  2.26  0.64   1.43 |  1.63  0.33   1.69 | -0.51
## CHULUCANAS-IRWIN      |  1.00 |  0.68  0.46   0.43 | -0.03  0.00  -0.03 |  0.67
## CHULUCANAS-JULIE      |  2.21 |  1.83  0.69   1.16 | -1.06  0.23  -1.09 | -0.63
##                        cos2 v.test  
## CHATO-CHATO            0.04  -0.71 |
## CHATO-CHULUCANAS       0.47   1.90 |
## CHATO-IRWIN            0.03   0.59 |
## CHATO-JULIE            0.01  -0.31 |
## CHULUCANAS-CHATO       0.07  -0.68 |
## CHULUCANAS-CHULUCANAS  0.03  -0.84 |
## CHULUCANAS-IRWIN       0.44   1.10 |
## CHULUCANAS-JULIE       0.08  -1.04 |

f2a <- plot.PCA(x = pca, choix = "var"
                , cex=0.8
                , label = "var"
                )

f2b <- plot.PCA(x = pca, choix = "ind"
                , habillage = 1
                , invisible = c("ind")
                , cex=0.8
                ) 

5.1.7 Figure 4

Principal Component Analysis (PCA).

fig <- list(f2a, f2b) %>% 
  plot_grid(plotlist = ., ncol = 2, nrow = 1
            , labels = "auto"
            , rel_widths = c(1, 1.5)
            ) 
fig %>% 
  ggsave2(plot = ., "submission/Figure_4.jpg", units = "cm"
          , width = 25, height = 10
          ) 

fig %>% 
  ggsave2(plot = ., "submission/Figure_4.eps", units = "cm"
          , width = 25, height = 10
          ) 

knitr::include_graphics("submission/Figure_4.jpg")

5.1.8 Supplementary Figure 1

Results of the contributions and correlation of the variables in the Principal Component Analysis (PCA).

var <- get_pca_var(pca)

pt1 <- fviz_eig(pca, 
                addlabels=TRUE,
                hjust = 0.05,
                barfill="white",
                barcolor ="darkblue",
                linecolor ="red") + 
  ylim(0, 80) + 
  labs(
    title = "PCA - percentage of explained variances",
    y = "Variance (%)") +
  theme_minimal()

pt2 <- fviz_contrib(pca,
                     choice = "var", 
                     axes = 1, 
                     top = 10,
                     fill="white",
                     color ="darkblue",
                     sort.val = "desc") +
  ylim(0, 50) + 
  labs(title = "Dim 1 - variables contribution") 

pt3 <- fviz_contrib(pca,
                     choice = "var", 
                     axes = 2, 
                     top = 10,
                     fill="white",
                     color ="darkblue",
                     sort.val = "desc") +
  ylim(0, 80) + 
  labs(title = "Dim 2 - variables contribution") 

pt4 <- ~ {
  
  corrplot(var$cor, 
         method="number",
         tl.col="black", 
         tl.srt=45,
         )
  
}

plot <- list(pt1, pt2, pt3) %>% 
  plot_grid(plotlist = ., ncol = 1, labels = "auto") %>% 
  list(., pt4) %>% 
  plot_grid(plotlist = ., ncol = 2, labels = c("", "d"))


ggsave2(plot = plot, "submission/FigS1.jpg", height = 20, width = 28, units = "cm")

ggsave2(plot = plot, "submission/FigS1.eps", height = 20, width = 28, units = "cm")

knitr::include_graphics("submission/FigS1.jpg")

5.2 Specific Objective 2

Determine the effect of the rootstock-interstock interaction on the fruit biometrics of mango.

5.2.1 Fruit Weigth

trait <- "weigth"

lmm <- paste({{trait}}, "~ 1 + (1|block) + stock*edge") %>% as.formula()

lmd <- paste({{trait}}, "~ block + stock*edge") %>% as.formula()

rmout <- fru %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot

rmout$outliers
##  [1] index       block       stock       edge        weigth      resi       
##  [7] res_MAD     rawp.BHStud adjp        bholm       out_flag   
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: weigth
##             Df  Sum Sq Mean Sq F value  Pr(>F)  
## block        2   73498   36749  4.6207 0.01078 *
## stock        1    1816    1816  0.2284 0.63320  
## edge         3    4318    1439  0.1810 0.90923  
## stock:edge   3   37870   12623  1.5872 0.19323  
## Residuals  229 1821257    7953                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ edge|stock) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()

	edge	stock	emmean	SE	df	lower.CL	upper.CL	group
1	IRWIN	CHATO	482.0333	16.28198	229	449.9517	514.1150	a
2	JULIE	CHATO	465.9667	16.28198	229	433.8850	498.0483	a
4	CHULUCANAS	CHATO	462.3661	16.56272	229	429.7313	495.0009	a
3	CHATO	CHATO	452.5333	16.28198	229	420.4517	484.6150	a
7	CHATO	CHULUCANAS	484.9667	16.28198	229	452.8850	517.0483	a
6	JULIE	CHULUCANAS	484.7000	16.28198	229	452.6184	516.7816	a
8	CHULUCANAS	CHULUCANAS	468.1667	16.28198	229	436.0850	500.2483	a
5	IRWIN	CHULUCANAS	447.2333	16.28198	229	415.1517	479.3150	a

p2a <- mc %>% 
  plot_smr(x = "stock"
           , y = "emmean"
           , group = "edge"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Rootstock"
           , ylab = "Fruit Weigth (g)"
           , glab = "Interstock"
           , ylimits = c(0, 600, 100)
           , 
           )

p2a

5.2.2 Fruit length

trait <- "long"

lmm <- paste({{trait}}, "~ 1 + (1|block) + stock*edge") %>% as.formula()

lmd <- paste({{trait}}, "~ block + stock*edge") %>% as.formula()

rmout <- fru %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot

rmout$outliers
##  [1] index       block       stock       edge        long        resi       
##  [7] res_MAD     rawp.BHStud adjp        bholm       out_flag   
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: long
##             Df  Sum Sq Mean Sq F value   Pr(>F)   
## block        2   681.4  340.70  5.9312 0.003081 **
## stock        1    31.2   31.16  0.5424 0.462192   
## edge         3   270.1   90.02  1.5672 0.198107   
## stock:edge   3    39.5   13.15  0.2289 0.876184   
## Residuals  229 13154.4   57.44                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ edge|stock) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()

	edge	stock	emmean	SE	df	lower.CL	upper.CL	group
1	JULIE	CHATO	107.6667	1.383746	229	104.9402	110.3932	a
3	CHULUCANAS	CHATO	106.3048	1.407606	229	103.5313	109.0783	a
2	IRWIN	CHATO	106.0667	1.383746	229	103.3402	108.7932	a
4	CHATO	CHATO	105.9667	1.383746	229	103.2402	108.6932	a
6	JULIE	CHULUCANAS	109.7333	1.383746	229	107.0068	112.4598	a
7	CHATO	CHULUCANAS	106.5333	1.383746	229	103.8068	109.2598	a
5	IRWIN	CHULUCANAS	106.4333	1.383746	229	103.7068	109.1598	a
8	CHULUCANAS	CHULUCANAS	106.2000	1.383746	229	103.4735	108.9265	a

p2b <- mc %>% 
  plot_smr(x = "stock"
           , y = "emmean"
           , group = "edge"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Rootstock"
           , ylab = "Fruit length (mm)"
           , glab = "Interstock"
           , ylimits = c(0, 120, 20)
           , 
           )

p2b

5.2.3 Fruit diameter

trait <- "diameter_average"

lmm <- paste({{trait}}, "~ 1 + (1|block) + stock*edge") %>% as.formula()

lmd <- paste({{trait}}, "~ block + stock*edge") %>% as.formula()

rmout <- fru %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot

rmout$outliers
##  [1] index            block            stock            edge            
##  [5] diameter_average resi             res_MAD          rawp.BHStud     
##  [9] adjp             bholm            out_flag        
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: diameter_average
##             Df Sum Sq Mean Sq F value   Pr(>F)   
## block        2  248.1 124.033  5.0489 0.007149 **
## stock        1    1.2   1.178  0.0480 0.826864   
## edge         3    6.1   2.027  0.0825 0.969504   
## stock:edge   3  234.3  78.109  3.1796 0.024804 * 
## Residuals  229 5625.6  24.566                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ edge|stock) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()

	edge	stock	emmean	SE	df	lower.CL	upper.CL	group
1	IRWIN	CHATO	87.63333	0.9049151	229	85.85031	89.41636	a
2	JULIE	CHATO	86.23333	0.9049151	229	84.45031	88.01636	a
4	CHULUCANAS	CHATO	86.07080	0.9205182	229	84.25703	87.88457	a
3	CHATO	CHATO	85.23333	0.9049151	229	83.45031	87.01636	a
7	CHATO	CHULUCANAS	87.70000	0.9049151	229	85.91698	89.48302	a
6	JULIE	CHULUCANAS	86.30000	0.9049151	229	84.51698	88.08302	a
8	CHULUCANAS	CHULUCANAS	86.08333	0.9049151	229	84.30031	87.86636	a
5	IRWIN	CHULUCANAS	84.53333	0.9049151	229	82.75031	86.31636	a

p2c <- mc %>% 
  plot_smr(x = "stock"
           , y = "emmean"
           , group = "edge"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Rootstock"
           , ylab = "Fruit diameter (mm)"
           , glab = "Interstock"
           , ylimits = c(0, 100, 20)
           , 
           )

p2c

5.2.4 Table 2

Descriptive statistics of the variables that determine the fruit biometrics of mango.

sts <- Summarize(weigth  ~ stock*edge, data = fru, digits = 2, na.rm = TRUE)

tb1a <- sts%>% 
  merge(., mc) %>% 
  mutate(Variable = "Fruit Weigth (g)") %>% 
  dplyr::select(Variable, stock, edge, mean, sd, min, max, group) %>% 
  rename(Rootstock = stock,
         Interstock = edge,
         Sig = group)

sts <- Summarize(long  ~ stock*edge, data = fru, digits = 2, na.rm = TRUE)

tb1b <- sts%>% 
  merge(., mc) %>% 
  mutate(Variable = "Fruit length (mm)") %>% 
  dplyr::select(Variable, stock, edge, mean, sd, min, max, group) %>% 
  rename(Rootstock = stock,
         Interstock = edge,
         Sig = group)

sts <- Summarize(diameter_average ~ stock*edge, data = fru, digits = 2, na.rm = TRUE)

tb1c <- sts%>% 
  merge(., mc) %>% 
  mutate(Variable = "Fruit diameter (mm)") %>% 
  dplyr::select(Variable, stock, edge, mean, sd, min, max, group) %>% 
  rename(Rootstock = stock,
         Interstock = edge,
         Sig = group)

tb1 <- bind_rows(tb1a, tb1b, tb1c)

tb1 %>% kable(align = 'c')

Variable	Rootstock	Interstock	mean	sd	min	max	Sig
Fruit Weigth (g)	CHATO	CHATO	452.53	79.92	305.0	639.0	a
Fruit Weigth (g)	CHATO	CHULUCANAS	462.34	69.82	340.0	620.0	a
Fruit Weigth (g)	CHATO	IRWIN	482.03	87.30	350.0	700.0	a
Fruit Weigth (g)	CHATO	JULIE	465.97	92.07	300.0	645.0	a
Fruit Weigth (g)	CHULUCANAS	CHATO	484.97	101.98	316.0	765.0	a
Fruit Weigth (g)	CHULUCANAS	CHULUCANAS	468.17	118.73	230.0	665.0	a
Fruit Weigth (g)	CHULUCANAS	IRWIN	447.23	70.09	310.0	605.0	a
Fruit Weigth (g)	CHULUCANAS	JULIE	484.70	93.46	367.0	717.0	a
Fruit length (mm)	CHATO	CHATO	105.97	6.78	93.0	120.0	a
Fruit length (mm)	CHATO	CHULUCANAS	106.28	6.80	94.0	119.0	a
Fruit length (mm)	CHATO	IRWIN	106.07	6.47	95.0	124.0	a
Fruit length (mm)	CHATO	JULIE	107.67	7.54	91.0	120.0	a
Fruit length (mm)	CHULUCANAS	CHATO	106.53	7.21	95.0	123.0	a
Fruit length (mm)	CHULUCANAS	CHULUCANAS	106.20	10.87	86.0	126.0	a
Fruit length (mm)	CHULUCANAS	IRWIN	106.43	7.28	92.0	122.0	a
Fruit length (mm)	CHULUCANAS	JULIE	109.73	8.03	98.0	129.0	a
Fruit diameter (mm)	CHATO	CHATO	85.23	4.72	76.0	97.5	a
Fruit diameter (mm)	CHATO	CHULUCANAS	86.09	3.61	78.5	93.5	a
Fruit diameter (mm)	CHATO	IRWIN	87.63	5.07	79.5	100.0	a
Fruit diameter (mm)	CHATO	JULIE	86.23	5.56	75.5	97.5	a
Fruit diameter (mm)	CHULUCANAS	CHATO	87.70	5.40	78.0	100.5	a
Fruit diameter (mm)	CHULUCANAS	CHULUCANAS	86.08	6.61	69.5	99.0	a
Fruit diameter (mm)	CHULUCANAS	IRWIN	84.53	4.03	77.5	92.5	a
Fruit diameter (mm)	CHULUCANAS	JULIE	86.30	4.68	79.5	97.0	a

tb1 %>%
  write_sheet(ss = gs, sheet = "tb1")

5.2.5 Multivariate

Principal Component Analysis (PCA) of fruit biometrics in the mango crop based on the use of rootstock-interstock combinations.

mv <- fru %>% 
  group_by(stock, edge) %>% 
  summarise(across(where(is.numeric), ~ mean(., na.rm = T))) %>%
  dplyr::select(!c(diameter_1, diameter_2, n_fruits)) %>%
  unite("treat", stock:edge, sep = "-") %>% 
   rename(Treat = treat
         , Weight = weigth
         , Length = long
         , Diameter = diameter_average)
  
pca <- mv %>% 
  PCA(scale.unit = T, quali.sup = 1, graph = F) 

# summary

summary(pca, nbelements = Inf, nb.dec = 2)
## 
## Call:
## PCA(X = ., scale.unit = T, quali.sup = 1, graph = F) 
## 
## 
## Eigenvalues
##                       Dim.1  Dim.2  Dim.3
## Variance               2.01   0.97   0.02
## % of var.             66.97  32.23   0.79
## Cumulative % of var.  66.97  99.21 100.00
## 
## Individuals
##                          Dist   Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3
## 1                     |  1.71 | -1.70 17.91  0.98 | -0.22  0.60  0.02 | -0.01
## 2                     |  0.68 | -0.56  1.99  0.69 | -0.37  1.76  0.29 |  0.09
## 3                     |  1.85 |  1.38 11.78  0.56 | -1.23 19.60  0.44 |  0.00
## 4                     |  0.70 |  0.09  0.05  0.02 |  0.61  4.83  0.76 |  0.33
## 5                     |  1.93 |  1.70 17.92  0.78 | -0.91 10.79  0.22 |  0.00
## 6                     |  0.57 | -0.29  0.51  0.25 | -0.44  2.46  0.58 | -0.23
## 7                     |  2.33 | -2.29 32.54  0.96 |  0.45  2.60  0.04 | -0.06
## 8                     |  2.69 |  1.67 17.30  0.38 |  2.11 57.36  0.61 | -0.12
##                         ctr  cos2  
## 1                      0.03  0.00 |
## 2                      4.74  0.02 |
## 3                      0.00  0.00 |
## 4                     57.67  0.22 |
## 5                      0.00  0.00 |
## 6                     28.15  0.16 |
## 7                      2.18  0.00 |
## 8                      7.23  0.00 |
## 
## Variables
##                         Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3   ctr
## Weight                |  0.99 49.14  0.99 | -0.03  0.07  0.00 | -0.11 50.79
## Length                |  0.46 10.55  0.21 |  0.89 81.29  0.79 |  0.04  8.16
## Diameter              |  0.90 40.32  0.81 | -0.42 18.64  0.18 |  0.10 41.05
##                        cos2  
## Weight                 0.01 |
## Length                 0.00 |
## Diameter               0.01 |
## 
## Supplementary categories
##                          Dist   Dim.1  cos2 v.test   Dim.2  cos2 v.test   Dim.3
## CHATO-CHATO           |  1.71 | -1.70  0.98  -1.20 | -0.22  0.02  -0.22 | -0.01
## CHATO-CHULUCANAS      |  0.68 | -0.56  0.69  -0.40 | -0.37  0.29  -0.38 |  0.09
## CHATO-IRWIN           |  1.85 |  1.38  0.56   0.97 | -1.23  0.44  -1.25 |  0.00
## CHATO-JULIE           |  0.70 |  0.09  0.02   0.07 |  0.61  0.76   0.62 |  0.33
## CHULUCANAS-CHATO      |  1.93 |  1.70  0.78   1.20 | -0.91  0.22  -0.93 |  0.00
## CHULUCANAS-CHULUCANAS |  0.57 | -0.29  0.25  -0.20 | -0.44  0.58  -0.44 | -0.23
## CHULUCANAS-IRWIN      |  2.33 | -2.29  0.96  -1.61 |  0.45  0.04   0.46 | -0.06
## CHULUCANAS-JULIE      |  2.69 |  1.67  0.38   1.18 |  2.11  0.61   2.14 | -0.12
##                        cos2 v.test  
## CHATO-CHATO            0.00  -0.05 |
## CHATO-CHULUCANAS       0.02   0.62 |
## CHATO-IRWIN            0.00  -0.02 |
## CHATO-JULIE            0.22   2.15 |
## CHULUCANAS-CHATO       0.00  -0.02 |
## CHULUCANAS-CHULUCANAS  0.16  -1.50 |
## CHULUCANAS-IRWIN       0.00  -0.42 |
## CHULUCANAS-JULIE       0.00  -0.76 |

f3a <- plot.PCA(x = pca, choix = "var"
                , cex=0.8
                )

f3b <- plot.PCA(x = pca, choix = "ind"
                , habillage = 1
                , invisible = c("ind")
                , cex=0.8
                ) 

5.2.6 Figure 5

Principal Component Analysis (PCA).

fig <- list(f3a, f3b) %>% 
  plot_grid(plotlist = ., ncol = 2, nrow = 1
            , labels = "auto"
            , rel_widths = c(1, 1.5)
            ) 
fig %>% 
  ggsave2(plot = ., "submission/Figure_5.jpg", units = "cm"
          , width = 25, height = 10
          ) 

fig %>% 
  ggsave2(plot = ., "submission/Figure_5.eps", units = "cm"
          , width = 25, height = 10
          ) 

knitr::include_graphics("submission/Figure_5.jpg")

5.2.7 Supplementary Figure 2

Results of the contributions and correlation of the variables in the Principal Component Analysis (PCA).

var <- get_pca_var(pca)

pt1 <- fviz_eig(pca, 
                addlabels=TRUE,
                hjust = 0.05,
                barfill="white",
                barcolor ="darkblue",
                linecolor = "white") + 
  ylim(0, 80) + 
  labs(
    title = "PCA - percentage of explained variances",
    y = "Variance (%)") +
  theme_minimal()

pt2 <- fviz_contrib(pca,
                     choice = "var", 
                     axes = 1, 
                     top = 10,
                     fill="white",
                     color ="darkblue",
                     sort.val = "desc") +
  ylim(0, 60) + 
  labs(title = "Dim 1 - variables contribution") 

pt3 <- fviz_contrib(pca,
                     choice = "var", 
                     axes = 2, 
                     top = 10,
                     fill="white",
                     color ="darkblue",
                     sort.val = "desc") +
  ylim(0, 100) + 
  labs(title = "Dim 2 - variables contribution") 

pt4 <- ~ {
  
  corrplot(var$cor, 
         method="number",
         tl.col="black", 
         tl.srt=45,
         )
  
}

plot <- list(pt1, pt2, pt3) %>% 
  plot_grid(plotlist = ., ncol = 1, labels = "auto") %>% 
  list(., pt4) %>% 
  plot_grid(plotlist = ., ncol = 2, labels = c("", "d"))


ggsave2(plot = plot, "submission/FigS2.jpg", height = 20, width = 30, units = "cm")

ggsave2(plot = plot, "submission/FigS2.eps", height = 20, width = 30, units = "cm")

knitr::include_graphics("submission/FigS2.jpg")